Comparative Study on Prediction of Diabetes Disease Using Various Machine Learning Models

DOI

10.2991/978-94-6463-787-8_24How to use a DOI?

Keywords

Diabetes prediction; Machine learning; SVM; KNN; DT; LR; RF

Abstract

This analysis aims to generate a sample that forecasts the possibility of diabetes in patients with the most elevated accuracy. In the end, four machine learning classification algorithms, applied logistic regression, DT, SVM, and KNN, are utilized in this investigation to catch diabetes in theearliest phase. Diabetes are one of the fatal and most established disorders that generates growth in blood sugar. There are numerous difficulties if diabetes is left untreated and unidentified, and the laborious identification technique leads to a patient calling a diagnostic facility and seeing a physician.Here, we also contrasted the accuracy of earlier research with the current work, which uses experiments on the Diabetes dataset that was obtained from Kaggle. All four algorithms’ performances are assessed utilizing a mixture of metrics, including ROC curve, F-1 score, precision, recall, and accuracy. Acc. to the results, Random Forest (RF) Classifiers perform more efficiently than additional algorithms, with the greatest accuracy of 97%. Receiver Operating Characteristic curve wasemployed appropriately and methodically to validate these findings.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Vaishnavi Vaishnavi
AU  - Ritesh Jha
AU  - Siba Mitra
PY  - 2025
DA  - 2025/07/17
TI  - Comparative Study on Prediction of Diabetes Disease Using Various Machine Learning Models
BT  - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
PB  - Atlantis Press
SP  - 285
EP  - 300
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-787-8_24
DO  - 10.2991/978-94-6463-787-8_24
ID  - Vaishnavi2025
ER  -