Comparative Analysis of Machine Learning-Based Diabetes Prediction Approaches

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Author(s): Harsh Vardhan (National Institute of Technology, Hamirpur, India)and Vijay Kumar (Dr B R Ambedkar National Institute of Technology, Jalandhar, India)
Copyright: 2024
Pages: 11
Source title: Future of AI in Medical Imaging
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Shobhit Tyagi (Sharda University, India)and Prashant Upadhyay (Sharda University, India)
DOI: 10.4018/979-8-3693-2359-5.ch005

Keywords: Artificial Intelligence / Computer Science & IT / Medical Information Science Reference / Medical Technologies

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Abstract

The early prediction of diabetes mellitus may help improve the health of patients and cure them of this disease. In recent years, machine learning techniques have been widely used to predict diabetes in its early stages. In this chapter, an attempt has been made to analyse the performance of different machine learning techniques for diabetic prediction. Four well-known machine learning techniques, named as random forest, support vector machine, decision tree, and XGBoost are used. These techniques are evaluated on the Indian Diabetes dataset. Experimental results reveal that random forest algorithm achieved highest accuracy than the other techniques in terms of performance measures. These techniques will help to reduce diabetes incidence and health care costs. This work can be used to envisage diabetes in its early stages.

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Comparative Analysis of Machine Learning-Based Diabetes Prediction Approaches

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