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DIABETES PREDICTION USING MACHINE LEARNING

Aman Toor1 | Dr. Vinay K Saini2| Er. Tejna Khosla2

1 B-tech scholar, Department of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India

2 Professor, Department of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India

Abstract

Diabetes mellitus is a chronic disease characterized by hyperglycaemia. It may cause many complications. According to the growing morbidity in recent years, in 2040, the world’s diabetic patients will reach 642 million, which means that one of the ten adults in the future is suffering from diabetes. There is no doubt that this alarming figure needs great attention. With the rapid development of machine learning, machine learning has been applied to many aspects of medical health. In this study, we used decision tree, random forest and support vector machine to predict diabetes mellitus. The dataset is the hospital physical examination data in Sylhet Diabetes Hospital in Sylhet, Bangladesh. This dataset contains 520 observations with 17 characteristics, collected using direct questionnaires and diagnosis results. In this study, we used principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) to reduce the dimensionality. The results showed that prediction with random forest could reach the highest accuracy (ACC = 0.97114) when all the attributes were used.