Title: Detailed comparative analysis of diabetes mellitus prediction using machine learning models

Authors: Arshiya Begum; Sreenivasa Reddy

Addresses: Computer Science and Engineering Department, Acharya Nagarjuna University, Guntur, India ' Computer Science and Engineering Department, Acharya Nagarjuna University, Guntur, India

Abstract: Today, millions of people are suffering from diabetes which contributes to many other lethal diseases i.e., heart, kidney, and nerve damage. Diabetes mellitus is a chronic disease characterised by the body's inability to metabolise glucose, which could be life-threatening. Thus, several researchers have attempted to construct an accurate diabetes predictive model over the years. Big data analytics has played a vital role in healthcare by building predictive models for diabetes mellitus using various machine learning techniques. A large amount of data is collected which opened the opportunity to develop more complex, accurate predictions of the model. This paper aims to discuss various machine learning models to predict diabetes mellitus more accurately over the years. We have conducted a thorough review of the literature on predicting diabetes using PIMA and other datasets, which demonstrates how various machine-learning algorithms can be used to predict diabetes.

Keywords: diabetes mellitus; DiabM; predictive model; big data analytics; machine learning model; hypoglycaemia.

DOI: 10.1504/IJKEDM.2023.135734

International Journal of Knowledge Engineering and Data Mining, 2023 Vol.8 No.1, pp.67 - 88

Received: 25 May 2023
Accepted: 13 Jun 2023
Published online: 03 Jan 2024 *

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