• Minda Sandhya
• Maddineni Sirisha
• Chintada Akhil
• Palaka Hima Sagar
• Dr.E.Sreenivasa Reddy

Diabetes is a common health problem that is typified by consistently elevated blood sugar levels, especially in Bangladesh. Heart attacks, strokes, kidney failure, and blindness are just a few of the major health issues it causes. The ability to take potentially life-saving action and intervene promptly is made possible by early detection. Regretfully, diabetes is becoming more and more common. The usage of this work is to analyze the predictive many widely used machine learning algorithms for diabetes. Technological developments in machine learning have yielded substantial benefits for the medical industry by providing a broad spectrum of algorithmic approaches. In this study, six popular machine learning approaches are employed to analyze performance measures.

supervised machine learning algorithms are SVM, Logistic Regression, Random Forest, Decision tree, KNN, and Navie Bayes theorem.

"Diabetes Prediction Using Supervised Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.a65-a76, June-2024, Available :http://www.jetir.org/papers/JETIR2406009.pdf

"Diabetes Prediction Using Supervised Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppa65-a76, June-2024, Available at : http://www.jetir.org/papers/JETIR2406009.pdf