• Sujith Naidu Boddeda
• Sankara Rao T

Diabetes mellitus is a chronic disease characterized by hyperglycemia. Diabetes is generally occurred due to genes, bad lifestyle, lack of exercise, overweight. Diabetes is one of the most populated diseases in the world according to WHO. Early prediction of diabetes helps us to reduce the risk factor and helps in improving treatment. The prediction of diabetes is sort of tough because of the smaller number of data availability along outliers within the data sets. In this we are proposing a robust framework for diabetes prediction where the outlier rejection, filling the missing values, data standardization, feature selection and different Machine Learning (ML) classifiers (k-nearest Neighbor, Decision Trees, Random Forest, Logistic regression, support vector machine). Along with this, we proposed the work of hybrid algorithm. In this we performed hybridization using different combination of algorithms for better accuracy the data set used is Pima Indian Diabetes Data set.

Machine learning, Diabetes, SVM, K-Nearest Neighbor, Decision Tree, Random Forest, Logistic regression, PIMA data set, Hybrid algorithm.

"Prediction Of Diabetes Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.i212-i219, May-2022, Available :http://www.jetir.org/papers/JETIR2205931.pdf

"Prediction Of Diabetes Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppi212-i219, May-2022, Available at : http://www.jetir.org/papers/JETIR2205931.pdf