Classification system on diabetes prediction using deep learning approach

Diabetes is considered one of the deadliest and most persistent diseases that cause glucose levels to increase. Complications can arise if diabetes is left untreated and undiagnosed. Patient visits specialist to determine the results of the patient status are usually to found. No matter how you look at it, the growth of methods to cope with AI is critical. There is a goal in this study to develop a model that can accurately predict the risk of diabetes in a patient population. Decision Tree, SVM, and Naive Bayes are used in this study to detect diabetes. The UCI AI library's Pima Indians Diabetes Database (PIDD) performs experiments. The presentation of each of the three computations is based on various factors, such as precision, accuracy, f-measure, and recall. Precision is measured by comparing instances to categorize the results as normal or abnormal and verify using the Receiver Operating Characteristic (ROC) curves.