Evaluating the Performance of Naive Bayes vs. Decision Tree in Predicting and Forecasting Diabetes

This paper investigates the comparative ability of Naive Bayes and Decision Tree algorithms in the prediction and forecasting of diabetes, a chronic condition that has an impact on millions of people worldwide. Prompt and precise prediction of diabetes is crucial for prompt action and efficient management. Machine learning algorithms, like Naive Bayes and Decision Tree, have become instrumental in enhancing the predictive accuracy of medical diagnoses.