Paper Title
Comparative Analysis of Implementation of Logistic Regression and Decision Tree in Prediction of Diabetes Mellitus

Abstract
Diabetes mellitus (DM), commonly referred to as just diabetes, is a set of metabolic illnesses that result in persistently elevated blood sugar levels. The signs of this high blood sugar include more frequent urination, increasing thirst, and a higher appetite. Diabetes has a lot of complications if left untreated. Diabetes-related ketoacidosis and nonketotic hyperosmolar coma are examples of acute complications. Cardiovascular disease, stroke, failure of the kidneys, ulcers in the feet, and eye damage are examples of these serious long-term consequences. It is a result of either insufficient insulin produced by the pancreas or improper insulin usage by the body's cells. Using the Train Test Split and K-Fold cross validation approaches, we presented a comparison of the performances of the Logistic Regression and Decision Tree models in this paper. Keywords - Diabetes, Decision Tree, Logistic Regression, Machine Learning, K-Fold, Train-test split.