Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data

Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient’s clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Amin Ul Haq [verfasserIn] Jian Ping Li [verfasserIn] Jalaluddin Khan [verfasserIn] Muhammad Hammad Memon [verfasserIn] Shah Nazir [verfasserIn] Sultan Ahmad [verfasserIn] Ghufran Ahmad Khan [verfasserIn] Amjad Ali [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	diabetes disease feature selection e-healthcare decision tree performance machine learning

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 20(2020), 9, p 2649
Übergeordnetes Werk:	volume:20 ; year:2020 ; number:9, p 2649

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s20092649

Katalog-ID:	DOAJ032686897

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spelling	10.3390/s20092649 doi (DE-627)DOAJ032686897 (DE-599)DOAJ5d55e9ee825c42d58c34bbd35880c694 DE-627 ger DE-627 rakwb eng TP1-1185 Amin Ul Haq verfasserin aut Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient’s clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment. diabetes disease feature selection e-healthcare decision tree performance machine learning Chemical technology Jian Ping Li verfasserin aut Jalaluddin Khan verfasserin aut Muhammad Hammad Memon verfasserin aut Shah Nazir verfasserin aut Sultan Ahmad verfasserin aut Ghufran Ahmad Khan verfasserin aut Amjad Ali verfasserin aut In Sensors MDPI AG, 2003 20(2020), 9, p 2649 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:9, p 2649 https://doi.org/10.3390/s20092649 kostenfrei https://doaj.org/article/5d55e9ee825c42d58c34bbd35880c694 kostenfrei https://www.mdpi.com/1424-8220/20/9/2649 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 9, p 2649
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allfieldsGer	10.3390/s20092649 doi (DE-627)DOAJ032686897 (DE-599)DOAJ5d55e9ee825c42d58c34bbd35880c694 DE-627 ger DE-627 rakwb eng TP1-1185 Amin Ul Haq verfasserin aut Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient’s clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment. diabetes disease feature selection e-healthcare decision tree performance machine learning Chemical technology Jian Ping Li verfasserin aut Jalaluddin Khan verfasserin aut Muhammad Hammad Memon verfasserin aut Shah Nazir verfasserin aut Sultan Ahmad verfasserin aut Ghufran Ahmad Khan verfasserin aut Amjad Ali verfasserin aut In Sensors MDPI AG, 2003 20(2020), 9, p 2649 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:9, p 2649 https://doi.org/10.3390/s20092649 kostenfrei https://doaj.org/article/5d55e9ee825c42d58c34bbd35880c694 kostenfrei https://www.mdpi.com/1424-8220/20/9/2649 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 9, p 2649
allfieldsSound	10.3390/s20092649 doi (DE-627)DOAJ032686897 (DE-599)DOAJ5d55e9ee825c42d58c34bbd35880c694 DE-627 ger DE-627 rakwb eng TP1-1185 Amin Ul Haq verfasserin aut Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient’s clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment. diabetes disease feature selection e-healthcare decision tree performance machine learning Chemical technology Jian Ping Li verfasserin aut Jalaluddin Khan verfasserin aut Muhammad Hammad Memon verfasserin aut Shah Nazir verfasserin aut Sultan Ahmad verfasserin aut Ghufran Ahmad Khan verfasserin aut Amjad Ali verfasserin aut In Sensors MDPI AG, 2003 20(2020), 9, p 2649 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:20 year:2020 number:9, p 2649 https://doi.org/10.3390/s20092649 kostenfrei https://doaj.org/article/5d55e9ee825c42d58c34bbd35880c694 kostenfrei https://www.mdpi.com/1424-8220/20/9/2649 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2020 9, p 2649
language	English
source	In Sensors 20(2020), 9, p 2649 volume:20 year:2020 number:9, p 2649
sourceStr	In Sensors 20(2020), 9, p 2649 volume:20 year:2020 number:9, p 2649
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	diabetes disease feature selection e-healthcare decision tree performance machine learning Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Amin Ul Haq @@aut@@ Jian Ping Li @@aut@@ Jalaluddin Khan @@aut@@ Muhammad Hammad Memon @@aut@@ Shah Nazir @@aut@@ Sultan Ahmad @@aut@@ Ghufran Ahmad Khan @@aut@@ Amjad Ali @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
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issn	14248220
topic_title	TP1-1185 Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data diabetes disease feature selection e-healthcare decision tree performance machine learning
topic	misc TP1-1185 misc diabetes disease misc feature selection misc e-healthcare misc decision tree misc performance misc machine learning misc Chemical technology
topic_unstemmed	misc TP1-1185 misc diabetes disease misc feature selection misc e-healthcare misc decision tree misc performance misc machine learning misc Chemical technology
topic_browse	misc TP1-1185 misc diabetes disease misc feature selection misc e-healthcare misc decision tree misc performance misc machine learning misc Chemical technology
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title	Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data
ctrlnum	(DE-627)DOAJ032686897 (DE-599)DOAJ5d55e9ee825c42d58c34bbd35880c694
title_full	Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data
author_sort	Amin Ul Haq
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author_browse	Amin Ul Haq Jian Ping Li Jalaluddin Khan Muhammad Hammad Memon Shah Nazir Sultan Ahmad Ghufran Ahmad Khan Amjad Ali
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doi_str_mv	10.3390/s20092649
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title_sort	intelligent machine learning approach for effective recognition of diabetes in e-healthcare using clinical data
callnumber	TP1-1185
title_auth	Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data
abstract	Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient’s clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment.
abstractGer	Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient’s clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment.
abstract_unstemmed	Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient’s clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment.
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title_short	Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data
url	https://doi.org/10.3390/s20092649 https://doaj.org/article/5d55e9ee825c42d58c34bbd35880c694 https://www.mdpi.com/1424-8220/20/9/2649 https://doaj.org/toc/1424-8220
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author2	Jian Ping Li Jalaluddin Khan Muhammad Hammad Memon Shah Nazir Sultan Ahmad Ghufran Ahmad Khan Amjad Ali
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up_date	2024-07-03T13:30:29.061Z
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