Citation :

Ishu Pandey, Anuradha Misra, 2026. "Diabetes Prediction Using Machine Learning Techniques: A Comparative Study " ESP International Journal of Advancements in Science & Technology (ESP-IJAST)  Volume 4, Issue 2: 39-46

Abstract :

High blood glucose levels are a hallmark of diabetes, a chronic metabolic condition that can cause major side effects like heart disease, renal failure, nerve damage, and eyesight loss if undetected or untreated. Early identification and care are essential to lowering the risk and severity of problems connected to diabetes, which affects more than 537 million people worldwide. However, because of the disease's multifaceted nature and the complexity of patient data, fast and correct diagnosis is still difficult.

References :

[1] L. Xie, “Pima Indian Diabetes Database and Machine Learning Models for Diabetes Prediction,” Highlights in Science, Engineering and Technology, vol. 66, pp. 400–406, 2024.

[2] Y. Tian, “Machine Learning Models for Diabetes Prediction: Logistic Regression, SVM, Random Forest, and Neural Networks,” Theoretical and Natural Science, 2025.

[3] J. E. Priyatma and M. R. A. Sasmita, “Comparative Analysis of Random Forest and Support Vector Machine for Classifying Pima Indians Diabetes Dataset,” United International Journal for Research & Technology (UIJRT), vol. 6, no. 9, pp. 117–126, 2025.

[4] A. A. Ali, G. R. Galal, and H. S. Hassan, “Diabetes Prediction on Pima Indians Dataset Using Machine Learning Techniques,” International Journal of Environmental Sciences, 2025.

[5] Dhanashree S. Medhekar, Mayur P. Bote, Shruti D. Deshmukh, “Heart Disease Prediction System using Naive Bayes”, INTERNATIONAL JOURNAL OF ENHANCED RESEARCH IN SCIENCE TECHNOLOGY & ENGINEERING, VOL. 2, ISSUE 3, MARCH.-2013, pp 1-5

[6] Mr. Sunil Navadia, Mr. Jobin Thomas, Mr. Pintukumar Yadav, Ms. Shakila Shaikh, “Weather Prediction: A novel approach for measuring and analyzing weather data”, International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), (I-SMAC 2017), IEEE, pp 414-417

[7] Tasin, I., Nabil, T., Rahman, M. M., & Hossain, M. A. (2022). Diabetes prediction using machine learning and explainable AI. BioMed Research International, Article ID 2347326. https://pmc.ncbi.nlm.nih.gov/articles/PMC10107388/

[8] Introduction to Data Mining and Knowledge Discovery, Third Edition, Two Crowds Corporation, http://www.twocrows.com/introdm.pdf, accessed on 12 April 2009.

[9] Han, J., Kamber, M.: “Data Mining Concepts and Techniques”, Morgan Kaufmann Publishers, 2006.

[10] A. Gautam and P. Bedi, "MR-VSM: Map Reduce based vector SpaceModel for user profiling-an empirical study on News data," 015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, 2015, pp. 355-360.

[11] Evans, Hastings, and Peacock. "Statistical Distributions," 3rd Ed., John Wiley and Sons, 2000.

[12] Velleman, Paul and Hoaglin, David. "The ABC's of EDA: Applications, Basics, and Computing," 1981.

[13] Draper and Smith. "Applied Regression Analysis," 2nd ed., John Wiley and Sons, 1981.

[14] Efron and Gong. "A Leisurely Look at the Bootstrap, the Jackknife, and Cross Validation," The American Statistician, February 1983.

Keywords :

Diabetes Prediction, Machine Learning (Ml), Support Vector Machine (Svm), Pima Indians Diabetes Dataset, Classification, Healthcare Analytics.