Stacking with recursive feature elimination-isolation forest for classification of diabetes mellitus
Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/41438/1/Stacking%20with%20Recursive%20Feature%20Elimination.pdf |
| _version_ | 1811138424434851840 |
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| author | Nur Farahaina, Idris Mohd Arfian, Ismail Mohd Izham, Mohd Jaya Ashraf Osman, Ibrahim Abulfaraj, Anas W. Binzagr, Faisal |
| author_facet | Nur Farahaina, Idris Mohd Arfian, Ismail Mohd Izham, Mohd Jaya Ashraf Osman, Ibrahim Abulfaraj, Anas W. Binzagr, Faisal |
| author_sort | Nur Farahaina, Idris |
| collection | UMP |
| description | Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain. |
| first_indexed | 2024-09-25T03:49:58Z |
| format | Article |
| id | UMPir41438 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-09-25T03:49:58Z |
| publishDate | 2024 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | UMPir414382024-05-29T01:09:37Z http://umpir.ump.edu.my/id/eprint/41438/ Stacking with recursive feature elimination-isolation forest for classification of diabetes mellitus Nur Farahaina, Idris Mohd Arfian, Ismail Mohd Izham, Mohd Jaya Ashraf Osman, Ibrahim Abulfaraj, Anas W. Binzagr, Faisal QA75 Electronic computers. Computer science RC Internal medicine Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain. Public Library of Science 2024-05 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/41438/1/Stacking%20with%20Recursive%20Feature%20Elimination.pdf Nur Farahaina, Idris and Mohd Arfian, Ismail and Mohd Izham, Mohd Jaya and Ashraf Osman, Ibrahim and Abulfaraj, Anas W. and Binzagr, Faisal (2024) Stacking with recursive feature elimination-isolation forest for classification of diabetes mellitus. PLoS ONE, 19 (5). pp. 1-18. ISSN 1932-6203. (Published) https://doi.org/10.1371/journal.pone.0302595 https://doi.org/10.1371/journal.pone.0302595 |
| spellingShingle | QA75 Electronic computers. Computer science RC Internal medicine Nur Farahaina, Idris Mohd Arfian, Ismail Mohd Izham, Mohd Jaya Ashraf Osman, Ibrahim Abulfaraj, Anas W. Binzagr, Faisal Stacking with recursive feature elimination-isolation forest for classification of diabetes mellitus |
| title | Stacking with recursive feature elimination-isolation forest for classification of diabetes mellitus |
| title_full | Stacking with recursive feature elimination-isolation forest for classification of diabetes mellitus |
| title_fullStr | Stacking with recursive feature elimination-isolation forest for classification of diabetes mellitus |
| title_full_unstemmed | Stacking with recursive feature elimination-isolation forest for classification of diabetes mellitus |
| title_short | Stacking with recursive feature elimination-isolation forest for classification of diabetes mellitus |
| title_sort | stacking with recursive feature elimination isolation forest for classification of diabetes mellitus |
| topic | QA75 Electronic computers. Computer science RC Internal medicine |
| url | http://umpir.ump.edu.my/id/eprint/41438/1/Stacking%20with%20Recursive%20Feature%20Elimination.pdf |
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