• Arjun Kakade

Diabetes is one of the most widespread diseases in the whole world, affecting about 15% of the global population. An early diagnosis of diabetes can help a potential patient to employ preventive measures to forestall diabetes. This research paper aims at predicting whether a person has diabetes by implementing Machine Learning models such as SVM (Support Vector Machine), KNN (k-Nearest-Neighbors), and Decision Trees. It was observed that factors such as Body Mass Index, Age, and Blood insulin levels were fundamental in diagnosing diabetes. The dataset used is obtained from UC Irvine’s Machine Learning Repository which contained all the aforementioned features. Decision Tree was observed to have a higher accuracy score of 98.5%. in comparison to other algorithms

Medical Diagnosis; Support Vector Machine; Machine Learning; Diabetes Mellitus.

"Classification of Subjects Using Machine Learning Models for Diagnosis of Diabetes", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 12, page no.b717-b723, December-2022, Available :http://www.jetir.org/papers/JETIR2212180.pdf

"Classification of Subjects Using Machine Learning Models for Diagnosis of Diabetes", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 12, page no. ppb717-b723, December-2022, Available at : http://www.jetir.org/papers/JETIR2212180.pdf