Comparative Analysis of Diabetic Prediction Using Machine Learning Algorithms

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Published: Mar 8, 2024
DOI: https://doi.org/10.53555/jaz.v45iS4.4308
Keywords:
Diabetes mellitus, Logistic regression, Machine learning, Support vector machine, WEKA

Main Article Content

Ms. Madhuvanthi B
Dr. Baskaran T S

Abstract

Diabetes mellitus (DM) is a severe worldwide health problem, and its prevalence is quickly growing. It is a spectrum of metabolic illnesses definite by continually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of difficulties, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be mainly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to detect the strong ML algorithm to forecast it. For this numerous ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to this work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the research was implemented using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This research discussed performance matrices and error rates of classifiers for both datasets. The outcomes showed that for the PID database (PIDD), SVM works improved with an accuracy of 74% whereas for Germany RF and KNN work improved with 98.7% accuracy. This study can helps healthcare facilities and researchers in understanding the value and application of ML algorithms in predicting diabetes at an initial stage

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How to Cite
Ms. Madhuvanthi B, & Dr. Baskaran T S. (2024). Comparative Analysis of Diabetic Prediction Using Machine Learning Algorithms. Journal of Advanced Zoology, 45(S4), 465–477. https://doi.org/10.53555/jaz.v45iS4.4308
Issue
Vol. 45 No. S4 (2024): Special Issue For "Recent Trends In Computer Science and Applications, Commerce and Management"
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Articles
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biographies

Ms. Madhuvanthi B

Research Scholar, PG & Research Department of Computer Science, A. Veeriya Vandayar Memorial Sri Pushpam College (Autonomous), Poondi - 613503, Thanjavur,“Affiliated to Bharathidasan University, Tiruchirappalli-620024”, Tamil Nadu, India

Dr. Baskaran T S

Associate Professor & Research Supervisor,

PG & Research Department of Computer Science,

A Veeriya Vandayar Memorial Sri Pushpam College (Autonomous), Poondi - 613503, Thanjavur,

“Affiliated to Bharathidasan University, Tiruchirapalli-620024”, TamilNadu, India.

 

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