Diabetes prediction using machine learning tools

Diabetes is a chronic metabolic disease marked by elevated blood glucose levels that affects millions of people worldwide. Diabetes should not be overlooked since if left untreated, it can cause major damage to our heart, blood vessels, eyes, kidneys, nerves, and other organs of the human body. If diabetes is detected early enough, it can be managed. In this study, we will use Machine Learning Classification approaches to predict diabetes. Some of the methods utilised are Logistic Regression (LR), K-Nearest Neighbour (KNN), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). Each model's accuracy differs when compared to other models. With an accuracy score of 81.21, the model SVM outperforms the others in the comparative comparison. The results presented in this paper reveal that the model is capable of accurately predicting diabetes with a high level of accuracy.