Title: Comparative analysis of various supervised machine learning algorithms for the early prediction of type-II diabetes mellitus

Authors: Shahid Mohammad Ganie; Majid Bashir Malik

Addresses: Department of Computer Sciences, BGSB University, Rajouri, J&K, India ' Department of Computer Sciences, BGSB University, Rajouri, J&K, India

Abstract: Diabetes is one among the top 10 causes of death. Diabetes mellitus is a fatal disease that poses a unique and significant threat to millions of people over the globe. Despite the absolute truth about the statistical data of diabetes from various sources like the World Health Organization, International Diabetes Federation, American Diabetes Association, etc. there is a positive message that early prediction along with appropriate care, diabetes mellitus can be managed and its complications can also be prevented. Nowadays in healthcare sector, machine learning techniques are gaining immense importance through their analytical classification capabilities. Machine learning paradigms are being exploited by researchers for better prediction of diabetes to save human lives. In this paper, a comparison of different supervised machine learning classifiers based on the performance evaluation of various metrics for the early prediction of type-II diabetes mellitus (T2DM) has been performed. The experimental work has been successfully carried out using six machine learning classification algorithms. Among all classifiers, random forest (RF) performs better for predicting T2DM with an accuracy rate of 93.75%. In addition, ten-fold cross-validation method has been applied to remove the class biasness in the dataset.

Keywords: type-II diabetes mellitus; T2DM; machine learning; framework; logistic regression; LR; Naïve Bayes; NB; support vector machine; SVM; decision tree; DT; random forest; RF; artificial neural network; ANN.

DOI: 10.1504/IJMEI.2022.126519

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.6, pp.473 - 483

Received: 21 Aug 2020
Accepted: 24 Dec 2020
Published online: 28 Oct 2022 *

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