Nowadays, heart disease is the leading cause of death globally, with an estimated 610000 lives each year due to heart condition. One of the most common causes of heart disease is high blood pressure (HBP), fasting blood sugar (FBS), diabetes, cholesterol, Body mass Index (BMI), heart rate (HR). Diagnosis of heart disease is more prevalent nowadays; this involves a lot of accuracy and uncertainty due to the large-scale data decision-based on doctors may fail in some cases. Data mining is an intelligent diagnostic tool in healthcare. Thus, it is imperative to predict that each menace stage depends on age, sex, blood pressure, diabetes symptoms, what we can do for precaution by diagnosing the disease and proper treatment at the right moment. The purpose of the research work is to develop different predictive models using different forecasting measures and perform comparative analysis. In this work, we have used Cleveland and Statlog datasets with Naive Bayes (NB), K-Nearest Neighbors (KNN), and Logistic Regression (LR), Support vector machine (SVM), Decision Tree (DT), and Random Forest (RF) Classifier to develop various predictors. The experiment result shows that the random forest classifier gives better accuracy and results on both datasets when we perform a comparative analysis of them among all other classification models.