• Neha Thakur
• Virendra Singh

Nowadays from health care industries, a large volume of data is generating. It is necessary to collect, store and process this data to discover knowledge from it and utilize it to take significant decisions. Diabetes is a disease that occurs when your blood glucose, also called blood sugar, is too high. Blood glucose is your main source of energy and comes from the food you eat. Insulin, a hormone made by the pancreas, helps glucose from food to get into your cells to be used for energy. Sometimes your body doesn’t make enough or any insulin or doesn’t use insulin well;glucose then stays in your blood and doesn’t reach to your cells, which turns into diabetes.The objective of this research is to make use of various Machine Learning Algorithms,to predict the type2 diabetes. The Pima Indians Diabetes Datasets (PIDD) have been used to predict diabetes disease. This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. This paper discusses the Machine Learning approach for the prediction of diabetes. A performance comparison between different Machine Learning Algorithms i.e. Predictive Modelling, Decision tree, Logistic regression, Gradient Boosting is done. The main objective is to assess the correctness in classifying data with respect to the efficiency and effectiveness of each algorithm in terms of accuracy, precision, sensitivity, and specificity.

Machine learning, Predictive Modelling, Accuracy, Machine Learning, PIDD, Diabetes Prediction

"Diabetes Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.f490-f499, June-2021, Available :http://www.jetir.org/papers/JETIR2106770.pdf

"Diabetes Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppf490-f499, June-2021, Available at : http://www.jetir.org/papers/JETIR2106770.pdf