A Classification System for Diabetic Patients with Machine Learning Techniques

Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Di...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Vandana Rawat [verfasserIn] Suryakant [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Bagging Boosting techniques Diabetes mellitus (DM) Machine learning techniques Naive Bayes Classifier RobustBoost techniques Prediction

Übergeordnetes Werk:	In: International Journal of Mathematical, Engineering and Management Sciences - Ram Arti Publishers, 2019, 4(2019), 3, Seite 729-744
Übergeordnetes Werk:	volume:4 ; year:2019 ; number:3 ; pages:729-744

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.33889/IJMEMS.2019.4.3-057

Katalog-ID:	DOAJ076931544

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ076931544
003	DE-627
005	20230503143150.0
007	cr uuu---uuuuu
008	230228s2019 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.33889/IJMEMS.2019.4.3-057 \|2 doi
035			\|a (DE-627)DOAJ076931544
035			\|a (DE-599)DOAJ5565bc4fd93c443399fff24c784a09bb
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a QA1-939
100	0		\|a Vandana Rawat \|e verfasserin \|4 aut
245	1	2	\|a A Classification System for Diabetic Patients with Machine Learning Techniques
264		1	\|c 2019
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM.
650		4	\|a Bagging
650		4	\|a Boosting techniques
650		4	\|a Diabetes mellitus (DM)
650		4	\|a Machine learning techniques
650		4	\|a Naive Bayes Classifier
650		4	\|a RobustBoost techniques
650		4	\|a Prediction
653		0	\|a Technology
653		0	\|a T
653		0	\|a Mathematics
700	0		\|a Suryakant \|e verfasserin \|4 aut
773	0	8	\|i In \|t International Journal of Mathematical, Engineering and Management Sciences \|d Ram Arti Publishers, 2019 \|g 4(2019), 3, Seite 729-744 \|w (DE-627)1702066037 \|w (DE-600)3028195-7 \|x 24557749 \|7 nnns
773	1	8	\|g volume:4 \|g year:2019 \|g number:3 \|g pages:729-744
856	4	0	\|u https://doi.org/10.33889/IJMEMS.2019.4.3-057 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/5565bc4fd93c443399fff24c784a09bb \|z kostenfrei
856	4	0	\|u https://www.ijmems.in/assets//57-ijmems-ecs-53-vol.-4%2c-no.-3%2c-729%E2%80%93744%2c-2019.pdf \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2455-7749 \|y Journal toc \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2455-7749 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 4 \|j 2019 \|e 3 \|h 729-744

Indexfelder

author_variant	v r vr s
matchkey_str	article:24557749:2019----::casfctossefribtcainsihahn
hierarchy_sort_str	2019
callnumber-subject-code	QA
publishDate	2019
allfields	10.33889/IJMEMS.2019.4.3-057 doi (DE-627)DOAJ076931544 (DE-599)DOAJ5565bc4fd93c443399fff24c784a09bb DE-627 ger DE-627 rakwb eng QA1-939 Vandana Rawat verfasserin aut A Classification System for Diabetic Patients with Machine Learning Techniques 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM. Bagging Boosting techniques Diabetes mellitus (DM) Machine learning techniques Naive Bayes Classifier RobustBoost techniques Prediction Technology T Mathematics Suryakant verfasserin aut In International Journal of Mathematical, Engineering and Management Sciences Ram Arti Publishers, 2019 4(2019), 3, Seite 729-744 (DE-627)1702066037 (DE-600)3028195-7 24557749 nnns volume:4 year:2019 number:3 pages:729-744 https://doi.org/10.33889/IJMEMS.2019.4.3-057 kostenfrei https://doaj.org/article/5565bc4fd93c443399fff24c784a09bb kostenfrei https://www.ijmems.in/assets//57-ijmems-ecs-53-vol.-4%2c-no.-3%2c-729%E2%80%93744%2c-2019.pdf kostenfrei https://doaj.org/toc/2455-7749 Journal toc kostenfrei https://doaj.org/toc/2455-7749 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2019 3 729-744
spelling	10.33889/IJMEMS.2019.4.3-057 doi (DE-627)DOAJ076931544 (DE-599)DOAJ5565bc4fd93c443399fff24c784a09bb DE-627 ger DE-627 rakwb eng QA1-939 Vandana Rawat verfasserin aut A Classification System for Diabetic Patients with Machine Learning Techniques 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM. Bagging Boosting techniques Diabetes mellitus (DM) Machine learning techniques Naive Bayes Classifier RobustBoost techniques Prediction Technology T Mathematics Suryakant verfasserin aut In International Journal of Mathematical, Engineering and Management Sciences Ram Arti Publishers, 2019 4(2019), 3, Seite 729-744 (DE-627)1702066037 (DE-600)3028195-7 24557749 nnns volume:4 year:2019 number:3 pages:729-744 https://doi.org/10.33889/IJMEMS.2019.4.3-057 kostenfrei https://doaj.org/article/5565bc4fd93c443399fff24c784a09bb kostenfrei https://www.ijmems.in/assets//57-ijmems-ecs-53-vol.-4%2c-no.-3%2c-729%E2%80%93744%2c-2019.pdf kostenfrei https://doaj.org/toc/2455-7749 Journal toc kostenfrei https://doaj.org/toc/2455-7749 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2019 3 729-744
allfields_unstemmed	10.33889/IJMEMS.2019.4.3-057 doi (DE-627)DOAJ076931544 (DE-599)DOAJ5565bc4fd93c443399fff24c784a09bb DE-627 ger DE-627 rakwb eng QA1-939 Vandana Rawat verfasserin aut A Classification System for Diabetic Patients with Machine Learning Techniques 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM. Bagging Boosting techniques Diabetes mellitus (DM) Machine learning techniques Naive Bayes Classifier RobustBoost techniques Prediction Technology T Mathematics Suryakant verfasserin aut In International Journal of Mathematical, Engineering and Management Sciences Ram Arti Publishers, 2019 4(2019), 3, Seite 729-744 (DE-627)1702066037 (DE-600)3028195-7 24557749 nnns volume:4 year:2019 number:3 pages:729-744 https://doi.org/10.33889/IJMEMS.2019.4.3-057 kostenfrei https://doaj.org/article/5565bc4fd93c443399fff24c784a09bb kostenfrei https://www.ijmems.in/assets//57-ijmems-ecs-53-vol.-4%2c-no.-3%2c-729%E2%80%93744%2c-2019.pdf kostenfrei https://doaj.org/toc/2455-7749 Journal toc kostenfrei https://doaj.org/toc/2455-7749 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2019 3 729-744
allfieldsGer	10.33889/IJMEMS.2019.4.3-057 doi (DE-627)DOAJ076931544 (DE-599)DOAJ5565bc4fd93c443399fff24c784a09bb DE-627 ger DE-627 rakwb eng QA1-939 Vandana Rawat verfasserin aut A Classification System for Diabetic Patients with Machine Learning Techniques 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM. Bagging Boosting techniques Diabetes mellitus (DM) Machine learning techniques Naive Bayes Classifier RobustBoost techniques Prediction Technology T Mathematics Suryakant verfasserin aut In International Journal of Mathematical, Engineering and Management Sciences Ram Arti Publishers, 2019 4(2019), 3, Seite 729-744 (DE-627)1702066037 (DE-600)3028195-7 24557749 nnns volume:4 year:2019 number:3 pages:729-744 https://doi.org/10.33889/IJMEMS.2019.4.3-057 kostenfrei https://doaj.org/article/5565bc4fd93c443399fff24c784a09bb kostenfrei https://www.ijmems.in/assets//57-ijmems-ecs-53-vol.-4%2c-no.-3%2c-729%E2%80%93744%2c-2019.pdf kostenfrei https://doaj.org/toc/2455-7749 Journal toc kostenfrei https://doaj.org/toc/2455-7749 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2019 3 729-744
allfieldsSound	10.33889/IJMEMS.2019.4.3-057 doi (DE-627)DOAJ076931544 (DE-599)DOAJ5565bc4fd93c443399fff24c784a09bb DE-627 ger DE-627 rakwb eng QA1-939 Vandana Rawat verfasserin aut A Classification System for Diabetic Patients with Machine Learning Techniques 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM. Bagging Boosting techniques Diabetes mellitus (DM) Machine learning techniques Naive Bayes Classifier RobustBoost techniques Prediction Technology T Mathematics Suryakant verfasserin aut In International Journal of Mathematical, Engineering and Management Sciences Ram Arti Publishers, 2019 4(2019), 3, Seite 729-744 (DE-627)1702066037 (DE-600)3028195-7 24557749 nnns volume:4 year:2019 number:3 pages:729-744 https://doi.org/10.33889/IJMEMS.2019.4.3-057 kostenfrei https://doaj.org/article/5565bc4fd93c443399fff24c784a09bb kostenfrei https://www.ijmems.in/assets//57-ijmems-ecs-53-vol.-4%2c-no.-3%2c-729%E2%80%93744%2c-2019.pdf kostenfrei https://doaj.org/toc/2455-7749 Journal toc kostenfrei https://doaj.org/toc/2455-7749 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2019 3 729-744
language	English
source	In International Journal of Mathematical, Engineering and Management Sciences 4(2019), 3, Seite 729-744 volume:4 year:2019 number:3 pages:729-744
sourceStr	In International Journal of Mathematical, Engineering and Management Sciences 4(2019), 3, Seite 729-744 volume:4 year:2019 number:3 pages:729-744
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Bagging Boosting techniques Diabetes mellitus (DM) Machine learning techniques Naive Bayes Classifier RobustBoost techniques Prediction Technology T Mathematics
isfreeaccess_bool	true
container_title	International Journal of Mathematical, Engineering and Management Sciences
authorswithroles_txt_mv	Vandana Rawat @@aut@@ Suryakant @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	1702066037
id	DOAJ076931544
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ076931544</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503143150.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.33889/IJMEMS.2019.4.3-057</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ076931544</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ5565bc4fd93c443399fff24c784a09bb</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA1-939</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Vandana Rawat</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Classification System for Diabetic Patients with Machine Learning Techniques</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bagging</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Boosting techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diabetes mellitus (DM)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Naive Bayes Classifier</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">RobustBoost techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prediction</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Mathematics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Suryakant</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">International Journal of Mathematical, Engineering and Management Sciences</subfield><subfield code="d">Ram Arti Publishers, 2019</subfield><subfield code="g">4(2019), 3, Seite 729-744</subfield><subfield code="w">(DE-627)1702066037</subfield><subfield code="w">(DE-600)3028195-7</subfield><subfield code="x">24557749</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:4</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:729-744</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.33889/IJMEMS.2019.4.3-057</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/5565bc4fd93c443399fff24c784a09bb</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.ijmems.in/assets//57-ijmems-ecs-53-vol.-4%2c-no.-3%2c-729%E2%80%93744%2c-2019.pdf</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2455-7749</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2455-7749</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">4</subfield><subfield code="j">2019</subfield><subfield code="e">3</subfield><subfield code="h">729-744</subfield></datafield></record></collection>
callnumber-first	Q - Science
author	Vandana Rawat
spellingShingle	Vandana Rawat misc QA1-939 misc Bagging misc Boosting techniques misc Diabetes mellitus (DM) misc Machine learning techniques misc Naive Bayes Classifier misc RobustBoost techniques misc Prediction misc Technology misc T misc Mathematics A Classification System for Diabetic Patients with Machine Learning Techniques
authorStr	Vandana Rawat
ppnlink_with_tag_str_mv	@@773@@(DE-627)1702066037
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	DOAJ
remote_str	true
callnumber-label	QA1-939
illustrated	Not Illustrated
issn	24557749
topic_title	QA1-939 A Classification System for Diabetic Patients with Machine Learning Techniques Bagging Boosting techniques Diabetes mellitus (DM) Machine learning techniques Naive Bayes Classifier RobustBoost techniques Prediction
topic	misc QA1-939 misc Bagging misc Boosting techniques misc Diabetes mellitus (DM) misc Machine learning techniques misc Naive Bayes Classifier misc RobustBoost techniques misc Prediction misc Technology misc T misc Mathematics
topic_unstemmed	misc QA1-939 misc Bagging misc Boosting techniques misc Diabetes mellitus (DM) misc Machine learning techniques misc Naive Bayes Classifier misc RobustBoost techniques misc Prediction misc Technology misc T misc Mathematics
topic_browse	misc QA1-939 misc Bagging misc Boosting techniques misc Diabetes mellitus (DM) misc Machine learning techniques misc Naive Bayes Classifier misc RobustBoost techniques misc Prediction misc Technology misc T misc Mathematics
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	International Journal of Mathematical, Engineering and Management Sciences
hierarchy_parent_id	1702066037
hierarchy_top_title	International Journal of Mathematical, Engineering and Management Sciences
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)1702066037 (DE-600)3028195-7
title	A Classification System for Diabetic Patients with Machine Learning Techniques
ctrlnum	(DE-627)DOAJ076931544 (DE-599)DOAJ5565bc4fd93c443399fff24c784a09bb
title_full	A Classification System for Diabetic Patients with Machine Learning Techniques
author_sort	Vandana Rawat
journal	International Journal of Mathematical, Engineering and Management Sciences
journalStr	International Journal of Mathematical, Engineering and Management Sciences
callnumber-first-code	Q
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2019
contenttype_str_mv	txt
container_start_page	729
author_browse	Vandana Rawat Suryakant
container_volume	4
class	QA1-939
format_se	Elektronische Aufsätze
author-letter	Vandana Rawat
doi_str_mv	10.33889/IJMEMS.2019.4.3-057
author2-role	verfasserin
title_sort	classification system for diabetic patients with machine learning techniques
callnumber	QA1-939
title_auth	A Classification System for Diabetic Patients with Machine Learning Techniques
abstract	Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM.
abstractGer	Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM.
abstract_unstemmed	Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	3
title_short	A Classification System for Diabetic Patients with Machine Learning Techniques
url	https://doi.org/10.33889/IJMEMS.2019.4.3-057 https://doaj.org/article/5565bc4fd93c443399fff24c784a09bb https://www.ijmems.in/assets//57-ijmems-ecs-53-vol.-4%2c-no.-3%2c-729%E2%80%93744%2c-2019.pdf https://doaj.org/toc/2455-7749
remote_bool	true
author2	Suryakant
author2Str	Suryakant
ppnlink	1702066037
callnumber-subject	QA - Mathematics
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.33889/IJMEMS.2019.4.3-057
callnumber-a	QA1-939
up_date	2024-07-03T23:08:47.105Z
_version_	1803601184944029696
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ076931544</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503143150.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.33889/IJMEMS.2019.4.3-057</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ076931544</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ5565bc4fd93c443399fff24c784a09bb</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA1-939</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Vandana Rawat</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Classification System for Diabetic Patients with Machine Learning Techniques</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Diabetes mellitus (DM) is a group of metallic disorder characterized by steep levels of blood glucose prolonged over a time. It results the defection in insulin production or improper action of the cells to the insulin produced. It is one of the significant public health care challenge worldwide. Diabetes exists in a body when pancreas does not construct enough hormone insulin or the human body is not being able to use the insulin properly. The diagnosis of diabetes (diagnosis, etiopathophysiology, therapy etc.) need to generate and process the vast amount of data. Data mining techniques have proven its usefulness and effectiveness in order to evaluate the unknown relationships or patterns if exists with such vast data. In the present work, five techniques based on machine learning namely, AdaBoost, LogicBoost, RobustBoost, Naïve Bayes and Bagging have been proposed for the analysis and prediction of DM patients. The proposed techniques are employed on the data set of Pima Indians Diabetes patients. The results computed are found to be very accurate with classification accuracy of 81.77% and 79.69% by bagging and AdaBoost techniques, respectively. Hence, the proposed techniques employed here are highly adorable, effective and efficient in order to predict the DM.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bagging</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Boosting techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diabetes mellitus (DM)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Naive Bayes Classifier</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">RobustBoost techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prediction</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Mathematics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Suryakant</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">International Journal of Mathematical, Engineering and Management Sciences</subfield><subfield code="d">Ram Arti Publishers, 2019</subfield><subfield code="g">4(2019), 3, Seite 729-744</subfield><subfield code="w">(DE-627)1702066037</subfield><subfield code="w">(DE-600)3028195-7</subfield><subfield code="x">24557749</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:4</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:729-744</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.33889/IJMEMS.2019.4.3-057</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/5565bc4fd93c443399fff24c784a09bb</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.ijmems.in/assets//57-ijmems-ecs-53-vol.-4%2c-no.-3%2c-729%E2%80%93744%2c-2019.pdf</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2455-7749</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2455-7749</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">4</subfield><subfield code="j">2019</subfield><subfield code="e">3</subfield><subfield code="h">729-744</subfield></datafield></record></collection>
score	7.3998833

Nicht das Richtige dabei?

Schreiben Sie uns!

A Classification System for Diabetic Patients with Machine Learning Techniques

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?