A soft computing approach for diabetes disease classification
As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordin...
| Main Authors: | , , , , |
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| Format: | Article |
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SAGE Publications Ltd
2018
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| _version_ | 1796864273720279040 |
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| author | Nilashi, Mehrbakhsh Ibrahim, Othman Mardani, Abbas Ahani, Ali Jusoh, Ahmad |
| author_facet | Nilashi, Mehrbakhsh Ibrahim, Othman Mardani, Abbas Ahani, Ali Jusoh, Ahmad |
| author_sort | Nilashi, Mehrbakhsh |
| collection | ePrints |
| description | As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system. |
| first_indexed | 2024-03-05T20:39:24Z |
| format | Article |
| id | utm.eprints-86591 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:39:24Z |
| publishDate | 2018 |
| publisher | SAGE Publications Ltd |
| record_format | dspace |
| spelling | utm.eprints-865912020-09-30T08:43:55Z http://eprints.utm.my/86591/ A soft computing approach for diabetes disease classification Nilashi, Mehrbakhsh Ibrahim, Othman Mardani, Abbas Ahani, Ali Jusoh, Ahmad QA75 Electronic computers. Computer science As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system. SAGE Publications Ltd 2018-12-01 Article PeerReviewed Nilashi, Mehrbakhsh and Ibrahim, Othman and Mardani, Abbas and Ahani, Ali and Jusoh, Ahmad (2018) A soft computing approach for diabetes disease classification. Health Informatics Journal, 24 (4). pp. 379-393. ISSN 1460-4582 http://dx.doi.org/10.1177/1460458216675500 DOI:10.1177/1460458216675500 |
| spellingShingle | QA75 Electronic computers. Computer science Nilashi, Mehrbakhsh Ibrahim, Othman Mardani, Abbas Ahani, Ali Jusoh, Ahmad A soft computing approach for diabetes disease classification |
| title | A soft computing approach for diabetes disease classification |
| title_full | A soft computing approach for diabetes disease classification |
| title_fullStr | A soft computing approach for diabetes disease classification |
| title_full_unstemmed | A soft computing approach for diabetes disease classification |
| title_short | A soft computing approach for diabetes disease classification |
| title_sort | soft computing approach for diabetes disease classification |
| topic | QA75 Electronic computers. Computer science |
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