Critical Factor Analysis for prediction of Diabetes Mellitus using an Inclusive Feature Selection Strategy
ABSTRACTDiabetes mellitus is a metabolic disorder that significantly implicates serious consequences in various parts of the human body, such as the Eye, Heart, kidney, Nerves, Foot, etc. The identification of consistent features significantly helps us to assess their impact on various organs of the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2331919 |
| _version_ | 1797230424819236864 |
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| author | E. Sreehari L. D. Dhinesh Babu |
| author_facet | E. Sreehari L. D. Dhinesh Babu |
| author_sort | E. Sreehari |
| collection | DOAJ |
| description | ABSTRACTDiabetes mellitus is a metabolic disorder that significantly implicates serious consequences in various parts of the human body, such as the Eye, Heart, kidney, Nerves, Foot, etc. The identification of consistent features significantly helps us to assess their impact on various organs of the human body and prevent further damage when detected at an early stage. The selection of appropriate features in the data set has potential benefits such as accuracy, minimizing complexity in terms of storage, computation, and positive decision-making. The left features might contain potential information that would be useful for analysis. In order to do effective analysis, additionally, all features should be studied and analyzed in plausible ways, such as using more feature selection (FS) methods with and without standardization. This article focuses on analyzing the critical factors of diabetes by using univariate, wrapper, and brute force FS techniques. To identify critical features, we used info gain, chi-square, RFE, and correlation using the NIDDK data. Later, distinct machine learning models were applied to both phases of the feature sets. This study was carried out in two phases to evaluate the efficacy of the techniques employed. The performance has been assessed using accuracy, F1score, and recall metrics. |
| first_indexed | 2024-04-24T15:28:16Z |
| format | Article |
| id | doaj.art-e348b358f3e448a88b34810b724dcba9 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-04-24T15:28:16Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-e348b358f3e448a88b34810b724dcba92024-04-02T05:41:27ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2331919Critical Factor Analysis for prediction of Diabetes Mellitus using an Inclusive Feature Selection StrategyE. Sreehari0L. D. Dhinesh Babu1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaABSTRACTDiabetes mellitus is a metabolic disorder that significantly implicates serious consequences in various parts of the human body, such as the Eye, Heart, kidney, Nerves, Foot, etc. The identification of consistent features significantly helps us to assess their impact on various organs of the human body and prevent further damage when detected at an early stage. The selection of appropriate features in the data set has potential benefits such as accuracy, minimizing complexity in terms of storage, computation, and positive decision-making. The left features might contain potential information that would be useful for analysis. In order to do effective analysis, additionally, all features should be studied and analyzed in plausible ways, such as using more feature selection (FS) methods with and without standardization. This article focuses on analyzing the critical factors of diabetes by using univariate, wrapper, and brute force FS techniques. To identify critical features, we used info gain, chi-square, RFE, and correlation using the NIDDK data. Later, distinct machine learning models were applied to both phases of the feature sets. This study was carried out in two phases to evaluate the efficacy of the techniques employed. The performance has been assessed using accuracy, F1score, and recall metrics.https://www.tandfonline.com/doi/10.1080/08839514.2024.2331919 |
| spellingShingle | E. Sreehari L. D. Dhinesh Babu Critical Factor Analysis for prediction of Diabetes Mellitus using an Inclusive Feature Selection Strategy Applied Artificial Intelligence |
| title | Critical Factor Analysis for prediction of Diabetes Mellitus using an Inclusive Feature Selection Strategy |
| title_full | Critical Factor Analysis for prediction of Diabetes Mellitus using an Inclusive Feature Selection Strategy |
| title_fullStr | Critical Factor Analysis for prediction of Diabetes Mellitus using an Inclusive Feature Selection Strategy |
| title_full_unstemmed | Critical Factor Analysis for prediction of Diabetes Mellitus using an Inclusive Feature Selection Strategy |
| title_short | Critical Factor Analysis for prediction of Diabetes Mellitus using an Inclusive Feature Selection Strategy |
| title_sort | critical factor analysis for prediction of diabetes mellitus using an inclusive feature selection strategy |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2331919 |
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