Thyroid prediction using ensemble data mining techniques
Abstract Data mining algorithms provide easy way to solve problem in medical data analysis. Data mining supports in complex data analysis to identify each issue in dataset. Now-a-days every person suffers for a good heath. The life style of every person is very fast so it is very difficult to mainta...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yadav, Dhyan Chandra [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Bharati Vidyapeeth's Institute of Computer Applications and Management 2019 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of information technology - [Singapore] : Springer Singapore, 2017, 14(2019), 3 vom: 28. Nov., Seite 1273-1283 |
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| Übergeordnetes Werk: |
volume:14 ; year:2019 ; number:3 ; day:28 ; month:11 ; pages:1273-1283 |
| Links: |
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| DOI / URN: |
10.1007/s41870-019-00395-7 |
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| Katalog-ID: |
SPR046964940 |
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| 520 | |a Abstract Data mining algorithms provide easy way to solve problem in medical data analysis. Data mining supports in complex data analysis to identify each issue in dataset. Now-a-days every person suffers for a good heath. The life style of every person is very fast so it is very difficult to maintain his health. Every person cannot easily maintain the hormone system in the body. Nowadays hormone disturbance are major issue in ladies. The major issues behind thyroids are hormonal disturbance. Initially we do not care the symptom of thyroids. If we have some knowledge about thyroid symptom then prior of major problem we protect his life. The symptoms of thyroid are very similar so we easily can not eliminate for identification. Data mining provide major help in thyroid dataset with different algorithms for classification, clustering, association etc. We use different types of machine learning meta classifier algorithms for thyroid dataset classification. In this analysis we analyze thyroid large and complex dataset by data mining meta classifier algorithm: Boosting, Bagging, Stacking and Voting with new ensemble model and con comparing classification accuracy, sensitivity and specificity. We easily classify thyroid dataset in different class level. Thyroid is a very common disease found in the human body, which is related to human diet and daily living with the help of classification algorithms, they can be avoided by studying various types of functions in thyroid, with the help of classification, after the disease consisting of experienced doctor walking expert system can be developed. | ||
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| 700 | 1 | |a Pal, Saurabh |4 aut | |
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10.1007/s41870-019-00395-7 doi (DE-627)SPR046964940 (SPR)s41870-019-00395-7-e DE-627 ger DE-627 rakwb eng Yadav, Dhyan Chandra verfasserin (orcid)0000-0003-0084-0360 aut Thyroid prediction using ensemble data mining techniques 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bharati Vidyapeeth's Institute of Computer Applications and Management 2019 Abstract Data mining algorithms provide easy way to solve problem in medical data analysis. Data mining supports in complex data analysis to identify each issue in dataset. Now-a-days every person suffers for a good heath. The life style of every person is very fast so it is very difficult to maintain his health. Every person cannot easily maintain the hormone system in the body. Nowadays hormone disturbance are major issue in ladies. The major issues behind thyroids are hormonal disturbance. Initially we do not care the symptom of thyroids. If we have some knowledge about thyroid symptom then prior of major problem we protect his life. The symptoms of thyroid are very similar so we easily can not eliminate for identification. Data mining provide major help in thyroid dataset with different algorithms for classification, clustering, association etc. We use different types of machine learning meta classifier algorithms for thyroid dataset classification. In this analysis we analyze thyroid large and complex dataset by data mining meta classifier algorithm: Boosting, Bagging, Stacking and Voting with new ensemble model and con comparing classification accuracy, sensitivity and specificity. We easily classify thyroid dataset in different class level. Thyroid is a very common disease found in the human body, which is related to human diet and daily living with the help of classification algorithms, they can be avoided by studying various types of functions in thyroid, with the help of classification, after the disease consisting of experienced doctor walking expert system can be developed. Data mining meta classifier algorithms (dpeaa)DE-He213 Boosting (dpeaa)DE-He213 Bagging (dpeaa)DE-He213 Stacking (dpeaa)DE-He213 Voting algorithms (dpeaa)DE-He213 Pal, Saurabh aut Enthalten in International journal of information technology [Singapore] : Springer Singapore, 2017 14(2019), 3 vom: 28. Nov., Seite 1273-1283 (DE-627)87523142X (DE-600)2878562-9 2511-2112 nnns volume:14 year:2019 number:3 day:28 month:11 pages:1273-1283 https://dx.doi.org/10.1007/s41870-019-00395-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2019 3 28 11 1273-1283 |
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10.1007/s41870-019-00395-7 doi (DE-627)SPR046964940 (SPR)s41870-019-00395-7-e DE-627 ger DE-627 rakwb eng Yadav, Dhyan Chandra verfasserin (orcid)0000-0003-0084-0360 aut Thyroid prediction using ensemble data mining techniques 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bharati Vidyapeeth's Institute of Computer Applications and Management 2019 Abstract Data mining algorithms provide easy way to solve problem in medical data analysis. Data mining supports in complex data analysis to identify each issue in dataset. Now-a-days every person suffers for a good heath. The life style of every person is very fast so it is very difficult to maintain his health. Every person cannot easily maintain the hormone system in the body. Nowadays hormone disturbance are major issue in ladies. The major issues behind thyroids are hormonal disturbance. Initially we do not care the symptom of thyroids. If we have some knowledge about thyroid symptom then prior of major problem we protect his life. The symptoms of thyroid are very similar so we easily can not eliminate for identification. Data mining provide major help in thyroid dataset with different algorithms for classification, clustering, association etc. We use different types of machine learning meta classifier algorithms for thyroid dataset classification. In this analysis we analyze thyroid large and complex dataset by data mining meta classifier algorithm: Boosting, Bagging, Stacking and Voting with new ensemble model and con comparing classification accuracy, sensitivity and specificity. We easily classify thyroid dataset in different class level. Thyroid is a very common disease found in the human body, which is related to human diet and daily living with the help of classification algorithms, they can be avoided by studying various types of functions in thyroid, with the help of classification, after the disease consisting of experienced doctor walking expert system can be developed. Data mining meta classifier algorithms (dpeaa)DE-He213 Boosting (dpeaa)DE-He213 Bagging (dpeaa)DE-He213 Stacking (dpeaa)DE-He213 Voting algorithms (dpeaa)DE-He213 Pal, Saurabh aut Enthalten in International journal of information technology [Singapore] : Springer Singapore, 2017 14(2019), 3 vom: 28. Nov., Seite 1273-1283 (DE-627)87523142X (DE-600)2878562-9 2511-2112 nnns volume:14 year:2019 number:3 day:28 month:11 pages:1273-1283 https://dx.doi.org/10.1007/s41870-019-00395-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2019 3 28 11 1273-1283 |
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10.1007/s41870-019-00395-7 doi (DE-627)SPR046964940 (SPR)s41870-019-00395-7-e DE-627 ger DE-627 rakwb eng Yadav, Dhyan Chandra verfasserin (orcid)0000-0003-0084-0360 aut Thyroid prediction using ensemble data mining techniques 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bharati Vidyapeeth's Institute of Computer Applications and Management 2019 Abstract Data mining algorithms provide easy way to solve problem in medical data analysis. Data mining supports in complex data analysis to identify each issue in dataset. Now-a-days every person suffers for a good heath. The life style of every person is very fast so it is very difficult to maintain his health. Every person cannot easily maintain the hormone system in the body. Nowadays hormone disturbance are major issue in ladies. The major issues behind thyroids are hormonal disturbance. Initially we do not care the symptom of thyroids. If we have some knowledge about thyroid symptom then prior of major problem we protect his life. The symptoms of thyroid are very similar so we easily can not eliminate for identification. Data mining provide major help in thyroid dataset with different algorithms for classification, clustering, association etc. We use different types of machine learning meta classifier algorithms for thyroid dataset classification. In this analysis we analyze thyroid large and complex dataset by data mining meta classifier algorithm: Boosting, Bagging, Stacking and Voting with new ensemble model and con comparing classification accuracy, sensitivity and specificity. We easily classify thyroid dataset in different class level. Thyroid is a very common disease found in the human body, which is related to human diet and daily living with the help of classification algorithms, they can be avoided by studying various types of functions in thyroid, with the help of classification, after the disease consisting of experienced doctor walking expert system can be developed. Data mining meta classifier algorithms (dpeaa)DE-He213 Boosting (dpeaa)DE-He213 Bagging (dpeaa)DE-He213 Stacking (dpeaa)DE-He213 Voting algorithms (dpeaa)DE-He213 Pal, Saurabh aut Enthalten in International journal of information technology [Singapore] : Springer Singapore, 2017 14(2019), 3 vom: 28. Nov., Seite 1273-1283 (DE-627)87523142X (DE-600)2878562-9 2511-2112 nnns volume:14 year:2019 number:3 day:28 month:11 pages:1273-1283 https://dx.doi.org/10.1007/s41870-019-00395-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2019 3 28 11 1273-1283 |
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10.1007/s41870-019-00395-7 doi (DE-627)SPR046964940 (SPR)s41870-019-00395-7-e DE-627 ger DE-627 rakwb eng Yadav, Dhyan Chandra verfasserin (orcid)0000-0003-0084-0360 aut Thyroid prediction using ensemble data mining techniques 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bharati Vidyapeeth's Institute of Computer Applications and Management 2019 Abstract Data mining algorithms provide easy way to solve problem in medical data analysis. Data mining supports in complex data analysis to identify each issue in dataset. Now-a-days every person suffers for a good heath. The life style of every person is very fast so it is very difficult to maintain his health. Every person cannot easily maintain the hormone system in the body. Nowadays hormone disturbance are major issue in ladies. The major issues behind thyroids are hormonal disturbance. Initially we do not care the symptom of thyroids. If we have some knowledge about thyroid symptom then prior of major problem we protect his life. The symptoms of thyroid are very similar so we easily can not eliminate for identification. Data mining provide major help in thyroid dataset with different algorithms for classification, clustering, association etc. We use different types of machine learning meta classifier algorithms for thyroid dataset classification. In this analysis we analyze thyroid large and complex dataset by data mining meta classifier algorithm: Boosting, Bagging, Stacking and Voting with new ensemble model and con comparing classification accuracy, sensitivity and specificity. We easily classify thyroid dataset in different class level. Thyroid is a very common disease found in the human body, which is related to human diet and daily living with the help of classification algorithms, they can be avoided by studying various types of functions in thyroid, with the help of classification, after the disease consisting of experienced doctor walking expert system can be developed. Data mining meta classifier algorithms (dpeaa)DE-He213 Boosting (dpeaa)DE-He213 Bagging (dpeaa)DE-He213 Stacking (dpeaa)DE-He213 Voting algorithms (dpeaa)DE-He213 Pal, Saurabh aut Enthalten in International journal of information technology [Singapore] : Springer Singapore, 2017 14(2019), 3 vom: 28. Nov., Seite 1273-1283 (DE-627)87523142X (DE-600)2878562-9 2511-2112 nnns volume:14 year:2019 number:3 day:28 month:11 pages:1273-1283 https://dx.doi.org/10.1007/s41870-019-00395-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2019 3 28 11 1273-1283 |
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10.1007/s41870-019-00395-7 doi (DE-627)SPR046964940 (SPR)s41870-019-00395-7-e DE-627 ger DE-627 rakwb eng Yadav, Dhyan Chandra verfasserin (orcid)0000-0003-0084-0360 aut Thyroid prediction using ensemble data mining techniques 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bharati Vidyapeeth's Institute of Computer Applications and Management 2019 Abstract Data mining algorithms provide easy way to solve problem in medical data analysis. Data mining supports in complex data analysis to identify each issue in dataset. Now-a-days every person suffers for a good heath. The life style of every person is very fast so it is very difficult to maintain his health. Every person cannot easily maintain the hormone system in the body. Nowadays hormone disturbance are major issue in ladies. The major issues behind thyroids are hormonal disturbance. Initially we do not care the symptom of thyroids. If we have some knowledge about thyroid symptom then prior of major problem we protect his life. The symptoms of thyroid are very similar so we easily can not eliminate for identification. Data mining provide major help in thyroid dataset with different algorithms for classification, clustering, association etc. We use different types of machine learning meta classifier algorithms for thyroid dataset classification. In this analysis we analyze thyroid large and complex dataset by data mining meta classifier algorithm: Boosting, Bagging, Stacking and Voting with new ensemble model and con comparing classification accuracy, sensitivity and specificity. We easily classify thyroid dataset in different class level. Thyroid is a very common disease found in the human body, which is related to human diet and daily living with the help of classification algorithms, they can be avoided by studying various types of functions in thyroid, with the help of classification, after the disease consisting of experienced doctor walking expert system can be developed. Data mining meta classifier algorithms (dpeaa)DE-He213 Boosting (dpeaa)DE-He213 Bagging (dpeaa)DE-He213 Stacking (dpeaa)DE-He213 Voting algorithms (dpeaa)DE-He213 Pal, Saurabh aut Enthalten in International journal of information technology [Singapore] : Springer Singapore, 2017 14(2019), 3 vom: 28. Nov., Seite 1273-1283 (DE-627)87523142X (DE-600)2878562-9 2511-2112 nnns volume:14 year:2019 number:3 day:28 month:11 pages:1273-1283 https://dx.doi.org/10.1007/s41870-019-00395-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2019 3 28 11 1273-1283 |
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Yadav, Dhyan Chandra @@aut@@ Pal, Saurabh @@aut@@ |
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Data mining supports in complex data analysis to identify each issue in dataset. Now-a-days every person suffers for a good heath. The life style of every person is very fast so it is very difficult to maintain his health. Every person cannot easily maintain the hormone system in the body. Nowadays hormone disturbance are major issue in ladies. The major issues behind thyroids are hormonal disturbance. Initially we do not care the symptom of thyroids. If we have some knowledge about thyroid symptom then prior of major problem we protect his life. The symptoms of thyroid are very similar so we easily can not eliminate for identification. Data mining provide major help in thyroid dataset with different algorithms for classification, clustering, association etc. We use different types of machine learning meta classifier algorithms for thyroid dataset classification. In this analysis we analyze thyroid large and complex dataset by data mining meta classifier algorithm: Boosting, Bagging, Stacking and Voting with new ensemble model and con comparing classification accuracy, sensitivity and specificity. We easily classify thyroid dataset in different class level. Thyroid is a very common disease found in the human body, which is related to human diet and daily living with the help of classification algorithms, they can be avoided by studying various types of functions in thyroid, with the help of classification, after the disease consisting of experienced doctor walking expert system can be developed.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data mining meta classifier algorithms</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Boosting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bagging</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stacking</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Voting algorithms</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pal, Saurabh</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of information technology</subfield><subfield code="d">[Singapore] : Springer Singapore, 2017</subfield><subfield code="g">14(2019), 3 vom: 28. 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Yadav, Dhyan Chandra |
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Thyroid prediction using ensemble data mining techniques Data mining meta classifier algorithms (dpeaa)DE-He213 Boosting (dpeaa)DE-He213 Bagging (dpeaa)DE-He213 Stacking (dpeaa)DE-He213 Voting algorithms (dpeaa)DE-He213 |
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thyroid prediction using ensemble data mining techniques |
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Thyroid prediction using ensemble data mining techniques |
| abstract |
Abstract Data mining algorithms provide easy way to solve problem in medical data analysis. Data mining supports in complex data analysis to identify each issue in dataset. Now-a-days every person suffers for a good heath. The life style of every person is very fast so it is very difficult to maintain his health. Every person cannot easily maintain the hormone system in the body. Nowadays hormone disturbance are major issue in ladies. The major issues behind thyroids are hormonal disturbance. Initially we do not care the symptom of thyroids. If we have some knowledge about thyroid symptom then prior of major problem we protect his life. The symptoms of thyroid are very similar so we easily can not eliminate for identification. Data mining provide major help in thyroid dataset with different algorithms for classification, clustering, association etc. We use different types of machine learning meta classifier algorithms for thyroid dataset classification. In this analysis we analyze thyroid large and complex dataset by data mining meta classifier algorithm: Boosting, Bagging, Stacking and Voting with new ensemble model and con comparing classification accuracy, sensitivity and specificity. We easily classify thyroid dataset in different class level. Thyroid is a very common disease found in the human body, which is related to human diet and daily living with the help of classification algorithms, they can be avoided by studying various types of functions in thyroid, with the help of classification, after the disease consisting of experienced doctor walking expert system can be developed. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2019 |
| abstractGer |
Abstract Data mining algorithms provide easy way to solve problem in medical data analysis. Data mining supports in complex data analysis to identify each issue in dataset. Now-a-days every person suffers for a good heath. The life style of every person is very fast so it is very difficult to maintain his health. Every person cannot easily maintain the hormone system in the body. Nowadays hormone disturbance are major issue in ladies. The major issues behind thyroids are hormonal disturbance. Initially we do not care the symptom of thyroids. If we have some knowledge about thyroid symptom then prior of major problem we protect his life. The symptoms of thyroid are very similar so we easily can not eliminate for identification. Data mining provide major help in thyroid dataset with different algorithms for classification, clustering, association etc. We use different types of machine learning meta classifier algorithms for thyroid dataset classification. In this analysis we analyze thyroid large and complex dataset by data mining meta classifier algorithm: Boosting, Bagging, Stacking and Voting with new ensemble model and con comparing classification accuracy, sensitivity and specificity. We easily classify thyroid dataset in different class level. Thyroid is a very common disease found in the human body, which is related to human diet and daily living with the help of classification algorithms, they can be avoided by studying various types of functions in thyroid, with the help of classification, after the disease consisting of experienced doctor walking expert system can be developed. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2019 |
| abstract_unstemmed |
Abstract Data mining algorithms provide easy way to solve problem in medical data analysis. Data mining supports in complex data analysis to identify each issue in dataset. Now-a-days every person suffers for a good heath. The life style of every person is very fast so it is very difficult to maintain his health. Every person cannot easily maintain the hormone system in the body. Nowadays hormone disturbance are major issue in ladies. The major issues behind thyroids are hormonal disturbance. Initially we do not care the symptom of thyroids. If we have some knowledge about thyroid symptom then prior of major problem we protect his life. The symptoms of thyroid are very similar so we easily can not eliminate for identification. Data mining provide major help in thyroid dataset with different algorithms for classification, clustering, association etc. We use different types of machine learning meta classifier algorithms for thyroid dataset classification. In this analysis we analyze thyroid large and complex dataset by data mining meta classifier algorithm: Boosting, Bagging, Stacking and Voting with new ensemble model and con comparing classification accuracy, sensitivity and specificity. We easily classify thyroid dataset in different class level. Thyroid is a very common disease found in the human body, which is related to human diet and daily living with the help of classification algorithms, they can be avoided by studying various types of functions in thyroid, with the help of classification, after the disease consisting of experienced doctor walking expert system can be developed. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2019 |
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Thyroid prediction using ensemble data mining techniques |
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https://dx.doi.org/10.1007/s41870-019-00395-7 |
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Pal, Saurabh |
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| doi_str |
10.1007/s41870-019-00395-7 |
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2024-07-04T01:14:42.400Z |
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