Classification of healthy and diabetic mellitus individuals by extracted textural features from left plantar thermograms and classifying using svm and nb classifiers
The aim of the study is to classify the healthy and diabetic mellitus individuals by extracting textural features from left plantar thermograms and classifying using support vector machine (SVM) and Naive Bayes (NB) classifiers. Materials and Methods: Images are collected from IEEE dataport, machine...
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| creator | Mounika, N. Thirunavukkarasu, Usharani |
| description | The aim of the study is to classify the healthy and diabetic mellitus individuals by extracting textural features from left plantar thermograms and classifying using support vector machine (SVM) and Naive Bayes (NB) classifiers. Materials and Methods: Images are collected from IEEE dataport, machine learning repository for healthy (n=21) and abnormal (n=21) to our study with alpha value as 0.05, 95% as CI, power as 80% and enrollment ratio as 1. The classification of diseased and healthy subjects was performed using WEKA, a data mining tool. The statistical analysis was performed using IBM SPSS software. Results: The performance of the classifiers were compared and found that the NB classifier has achieved 90.47% as classification accuracy rate than the SVM classifier. The independent Tsample test reveals that there is a significant difference between the groups (p |
| doi_str_mv | 10.1063/5.0173193 |
| format | Conference Proceeding |
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Karthick Anand ; Vijayan, V.</contributor><creatorcontrib>Mounika, N. ; Thirunavukkarasu, Usharani ; Srinivasan, R. ; Balasubramanian, PL ; Jeganathan, M. ; Sathish, T. ; Babu, A.B. Karthick Anand ; Vijayan, V.</creatorcontrib><description>The aim of the study is to classify the healthy and diabetic mellitus individuals by extracting textural features from left plantar thermograms and classifying using support vector machine (SVM) and Naive Bayes (NB) classifiers. Materials and Methods: Images are collected from IEEE dataport, machine learning repository for healthy (n=21) and abnormal (n=21) to our study with alpha value as 0.05, 95% as CI, power as 80% and enrollment ratio as 1. The classification of diseased and healthy subjects was performed using WEKA, a data mining tool. The statistical analysis was performed using IBM SPSS software. Results: The performance of the classifiers were compared and found that the NB classifier has achieved 90.47% as classification accuracy rate than the SVM classifier. The independent Tsample test reveals that there is a significant difference between the groups (p<0.01). Conclusion: The NB classifier shows the higher accuracy value when compared to the SVM classifier for predicting diabetes mellitus.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0173193</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Classification ; Classifiers ; Data mining ; Diabetes ; Diabetes mellitus ; Machine learning ; Statistical analysis ; Support vector machines</subject><ispartof>AIP conference proceedings, 2023, Vol.2822 (1)</ispartof><rights>Author(s)</rights><rights>2023 Author(s). Published by AIP Publishing.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubs.aip.org/acp/article-lookup/doi/10.1063/5.0173193$$EHTML$$P50$$Gscitation$$H</linktohtml><link.rule.ids>310,311,315,782,786,791,792,796,4516,23939,23940,25149,27933,27934,76394</link.rule.ids></links><search><contributor>Srinivasan, R.</contributor><contributor>Balasubramanian, PL</contributor><contributor>Jeganathan, M.</contributor><contributor>Sathish, T.</contributor><contributor>Babu, A.B. Karthick Anand</contributor><contributor>Vijayan, V.</contributor><creatorcontrib>Mounika, N.</creatorcontrib><creatorcontrib>Thirunavukkarasu, Usharani</creatorcontrib><title>Classification of healthy and diabetic mellitus individuals by extracted textural features from left plantar thermograms and classifying using svm and nb classifiers</title><title>AIP conference proceedings</title><description>The aim of the study is to classify the healthy and diabetic mellitus individuals by extracting textural features from left plantar thermograms and classifying using support vector machine (SVM) and Naive Bayes (NB) classifiers. Materials and Methods: Images are collected from IEEE dataport, machine learning repository for healthy (n=21) and abnormal (n=21) to our study with alpha value as 0.05, 95% as CI, power as 80% and enrollment ratio as 1. The classification of diseased and healthy subjects was performed using WEKA, a data mining tool. The statistical analysis was performed using IBM SPSS software. Results: The performance of the classifiers were compared and found that the NB classifier has achieved 90.47% as classification accuracy rate than the SVM classifier. The independent Tsample test reveals that there is a significant difference between the groups (p<0.01). Conclusion: The NB classifier shows the higher accuracy value when compared to the SVM classifier for predicting diabetes mellitus.</description><subject>Classification</subject><subject>Classifiers</subject><subject>Data mining</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Machine learning</subject><subject>Statistical analysis</subject><subject>Support vector machines</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNo1UclKBDEUDKLgOHrwDwLehB6zdHo5yuAGA14UvDUv6WQmQ3oxSQ_2B_mf9ixe3iteFVXwCqFbShaUZPxBLAjNOS35GZpRIWiSZzQ7RzNCyjRhKf-6RFchbAlhZZ4XM_S7dBCCNVZBtF2LO4M3GlzcjBjaGtcWpI5W4UY7Z-MQsG1ru7P1AC5gOWL9Ez2oqGscJzh4cNhomIAO2PiuwU6biHsHbQSP40b7plt7aMLBXh3DR9uu8RD2M-yaA9PKf9JqH67RhZkC9c1pz9Hn89PH8jVZvb-8LR9XSU85jwkVlGaEpURoDcBVKhkjUhWU6TQVRcoLIwkoBkSJ6UJyI0BmZtJRXtcy53N0d_Ttffc96BCrbTf4doqsWFGUecoELybV_VEVlI2Ht1W9tw34saKk2tdQiepUA_8D4Hh-dw</recordid><startdate>20231114</startdate><enddate>20231114</enddate><creator>Mounika, N.</creator><creator>Thirunavukkarasu, Usharani</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20231114</creationdate><title>Classification of healthy and diabetic mellitus individuals by extracted textural features from left plantar thermograms and classifying using svm and nb classifiers</title><author>Mounika, N. ; Thirunavukkarasu, Usharani</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p133t-1511602405eeaa3c4b220bc812e4458438fb0ac2a0c5e4407f5ab6fc4b13ddb73</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Classification</topic><topic>Classifiers</topic><topic>Data mining</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Machine learning</topic><topic>Statistical analysis</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mounika, N.</creatorcontrib><creatorcontrib>Thirunavukkarasu, Usharani</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mounika, N.</au><au>Thirunavukkarasu, Usharani</au><au>Srinivasan, R.</au><au>Balasubramanian, PL</au><au>Jeganathan, M.</au><au>Sathish, T.</au><au>Babu, A.B. Karthick Anand</au><au>Vijayan, V.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Classification of healthy and diabetic mellitus individuals by extracted textural features from left plantar thermograms and classifying using svm and nb classifiers</atitle><btitle>AIP conference proceedings</btitle><date>2023-11-14</date><risdate>2023</risdate><volume>2822</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>The aim of the study is to classify the healthy and diabetic mellitus individuals by extracting textural features from left plantar thermograms and classifying using support vector machine (SVM) and Naive Bayes (NB) classifiers. Materials and Methods: Images are collected from IEEE dataport, machine learning repository for healthy (n=21) and abnormal (n=21) to our study with alpha value as 0.05, 95% as CI, power as 80% and enrollment ratio as 1. The classification of diseased and healthy subjects was performed using WEKA, a data mining tool. The statistical analysis was performed using IBM SPSS software. Results: The performance of the classifiers were compared and found that the NB classifier has achieved 90.47% as classification accuracy rate than the SVM classifier. The independent Tsample test reveals that there is a significant difference between the groups (p<0.01). Conclusion: The NB classifier shows the higher accuracy value when compared to the SVM classifier for predicting diabetes mellitus.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0173193</doi><tpages>7</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2023, Vol.2822 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_scitation_primary_10_1063_5_0173193 |
| source | AIP Journals Complete |
| subjects | Classification Classifiers Data mining Diabetes Diabetes mellitus Machine learning Statistical analysis Support vector machines |
| title | Classification of healthy and diabetic mellitus individuals by extracted textural features from left plantar thermograms and classifying using svm and nb classifiers |
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