Title: A review on prediction of diabetes using machine learning and data mining classification techniques

Authors: Abhilash Pati; Manoranjan Parhi; Binod Kumar Pattanayak

Addresses: Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

Abstract: Machine learning (ML) and data mining (DM) techniques have grown in popularity among researchers and scientists in various fields. The healthcare industry could not be an exception to it. Diabetes or diabetes mellitus, a gaggle of metabolic disorder, can be caused due to age, obesity, lack of exercise, hereditary diabetes, living style, bad diet, hypertension, etc. and for that, the entire body system can be affected harmfully and be susceptible to dangerous diseases like heart disease, kidney disease, stroke, eye problem, nerve damage, etc. For this, we tried to go for a systematic review on diabetes by applying ML and DM classification algorithms for prediction and diagnosis. Concerning the sort of knowledge, medical datasets as well as Pima Indian Diabetes Datasets (PIDDs) provided by the UCI-ML Repository were mainly used. This survey may be useful for further investigation in predictions and resulting valuable knowledge on diabetes.

Keywords: diabetes mellitus; prediction; machine learning; ML; data mining; DM; classification techniques.

DOI: 10.1504/IJBET.2023.128514

International Journal of Biomedical Engineering and Technology, 2023 Vol.41 No.1, pp.83 - 109

Received: 21 Jul 2020
Accepted: 21 Dec 2020
Published online: 25 Jan 2023 *