• Harshada Vinod Ankam
• Anushka Urunkar
• Sonal Kale
• Poonam Dede
• Kiran Yesugade

Diabetes is a global health problem that affects patients for the rest of their lives. People of all ages groups are affected by diabetes. Technology development is a novel technique to predicting diabetes and providing more accurate and efficient results. The majority of the studies were conducted to predict diabetes, and the majority of the researchers used the Pima Indian dataset. The authors of this research develop a framework for estimating the chance of diabetes in patients with the greatest accuracy. The authors of this research develop a framework for estimating the chance of diabetes in patients with the greatest accuracy. To diagnose diabetes in its early stages, the developers of this proposed system employ Machine Learning techniques such as Random Forest, SVM, Logistics Regression, and KNN. The Kaggle and Pima Indian Datasets are used in this proposed system. This proposed system includes three different diabetes prediction modules. Diabetes Prediction Using Blood Reports, Gestational Diabetes Prediction, and Diabetes Prediction Using Retina Images Here are three alternative modules for Diabetes Prediction based on users' preferences: the first is a medical report check, the second is just for pregnant women, and the third is retinal diabetes Prediction.

Machine learning, XG Boost, Random Forest, KNN (K-Nearest Neighbors), SVM (Support Vector Machine), Logistic Regression, CNN (Convolutional Neural Network).

"MACHINE LEARNING BASED WEB APPLICATION FOR DIABETES PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.h488-h493, May-2022, Available :http://www.jetir.org/papers/JETIR2205863.pdf

"MACHINE LEARNING BASED WEB APPLICATION FOR DIABETES PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. pph488-h493, May-2022, Available at : http://www.jetir.org/papers/JETIR2205863.pdf