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Classification of Diabetes Mellitus: A Comparative Analysis of various Machine Learning Algorithms

by Esha Nadeem, Rameez Asif, Ali Raza, Adil Mustafa
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 129
Year of Publication: 2026
Authors: Esha Nadeem, Rameez Asif, Ali Raza, Adil Mustafa
10.5120/ijca544d1849f443

Esha Nadeem, Rameez Asif, Ali Raza, Adil Mustafa . Classification of Diabetes Mellitus: A Comparative Analysis of various Machine Learning Algorithms. International Journal of Computer Applications. 187, 129 ( Jul 2026), 1-9. DOI=10.5120/ijca544d1849f443

@article{ 10.5120/ijca544d1849f443,
author = { Esha Nadeem, Rameez Asif, Ali Raza, Adil Mustafa },
title = { Classification of Diabetes Mellitus: A Comparative Analysis of various Machine Learning Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2026 },
volume = { 187 },
number = { 129 },
month = { Jul },
year = { 2026 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number129/classification-of-diabetes-mellitus-a-comparative-analysis-of-various-machine-learning-algorithms/ },
doi = { 10.5120/ijca544d1849f443 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-07-29T00:34:38.600908+05:30
%A Esha Nadeem
%A Rameez Asif
%A Ali Raza
%A Adil Mustafa
%T Classification of Diabetes Mellitus: A Comparative Analysis of various Machine Learning Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 129
%P 1-9
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Diabetes Mellitus, a widespread metabolic disorder characterised by hyperglycaemia, poses a significant global health concern. Unfortunately, it is often diagnosed only after symptoms have developed, thereby reducing opportunities for early intervention. Traditional diagnostic methods primarily identify individuals who are already symptomatic, leaving many at-risk individuals undetected. By integrating machine learning models with Electronic Health Records, it may be possible to identify individuals at high risk during routine health assessments, enabling earlier intervention. This study aims to compare different machine learning algorithms to determine whether an individual has diabetes. Several performance metrics are used for comparison, including precision, accuracy, F1-score, recall, and computational efficiency. The methodology begins with the pre-processing of the Pima Indians Diabetes Dataset, followed by the training, testing, and validation of four machine learning algorithms: Support Vector Machine, Decision Tree, Artificial Neural Network, and Random Forest, implemented using Python. The process is then repeated in MATLAB using an augmented dataset, and the resulting performances are compared. The Random Forest algorithm achieved the highest prediction accuracy, with 75% accuracy on the original dataset and 100% accuracy on the augmented dataset. The findings suggest that machine learning approaches could support healthcare professionals in identifying individuals at risk of diabetes at an earlier stage, potentially enabling timely intervention and improving patient outcomes.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence; Machine Learning; Diabetes Mellitus; AI in Healthcare; Random Forest; Support Vector Machine; Artificial Neural Network Decision Tree

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