A soft computing approach for diabetes disease classification.

Clicks: 308
ID: 60965
2018
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Abstract
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.
Reference Key
nilashi2018ahealth Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nilashi, Mehrbakhsh;Bin Ibrahim, Othman;Mardani, Abbas;Ahani, Ali;Jusoh, Ahmad;
Journal Health informatics journal
Year 2018
DOI
10.1177/1460458216675500
URL
Keywords
Machine learning clustering diabetes disease diagnosis incremental principal component analysis incremental support vector machine

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