Machine Learning Techniques for improving study of Heart Disease Prediction System

The heart is an important organ in human beings. Because even a slight error might result in weariness or death, diagnosing and forecasting cardiac disorders requires increased precision, perfection, and accuracy. There are innumerable heart-related deaths, and the number is increasing significantly every day. To address the issue, researchers use a variety of data mining and machine learning approaches to evaluate massive amounts of complex medical data, assisting healthcare providers in the prediction of heart disease. Using various data mining approaches, the suggested research predicts the likelihood of heart disease and categorizes the risk level of patients. When compared to other machine learning algorithms, the trial results show that the bagging technique with decision tree algorithm has the highest accuracy of 88.56%. Keywords – Heart disease Classification, Machine Learning, Ensemble method