Published Paper Details:

Diabetes is a disease caused by a high level of glucose in the human body. Diabetes should not be ignored if it is not treated, then Diabetes can cause serious problems for a person such as: heart problems, kidney problems, high blood pressure, eye damage and can affect other parts of the human body. Curing diabetes can be easier if it is predicted earlier. In order to achieve this project goal we will be able to predict Diabetes in the human body or patient with the highest accuracy by applying, Various Machine Learning Strategies. Machine learning is used to predict accurate values with the available set of patient informations so the accurate values of the diabetes based on the individual can be predicted accurately. In this activity we will use the Learning Machine Planning and compile data to predict diabetes. Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Neighboring K-Nearest (KNN), Gradient Boosting (GB) and Random Forest (RF). Accuracy is different for all models compared to other models. Project Work gives a more exact or precise model appearance that the model can precisely foresee diabetes. Our outcome shows that Random Forest has accomplished higher exactness contrasted with other AI systems.