Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset

Fuzzy Information and Engineering

Othman Ibrahim¹  Leila Shahmoradi²  Mohammad Dalvi³  Mehrbakhsh Nilashi⁴  Hossein Ahmadi⁵ 

[1] Department of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran;Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran;Department of Mechatronics Engineering, University of Isfahan, Isfahan, Iran;Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia;Health Information Management Department, 5th Floor, School of Allied Medical Sciences, Tehran University of Medical Sciences, No. 17, Farredanesh Alley, Ghods St Enghelab Ave, Iran;

关键词: Diabetes disease diagnosis;    Clustering;    PCA;    Neural Network;   

DOI  :  10.1016/j.fiae.2017.09.006

来源: DOAJ

【 摘 要 】

As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. Providing diagnostic aid for diabetes disease by using a set of data that contains only medical information obtained without advanced medical equipment, can help numbers of people who want to discover the disease or the risk of disease at an early stage. This can possibly make a huge positive impact on a lot of peoples lives. 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 SOM, PCA and NN for clustering, noise removal and classification tasks, respectively. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction in relation to methods developed in the previous studies. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.

【 授权许可】

Unknown