Investigating The Role of Machine Learning For Heart Disease Prediction

Prediction of heart disease in the human body is an essential task in the field of medicine, as it prevents morbidity and mortality through early diagnosis and treatment. In this research, we investigate how well several machine learning algorithms can predict whether a patient has heart disease (via their presence [1] or absence [0]) based on the 14 selected features in the Statlog (Heart) data set. Here, we modified the algorithms with the help of hyper-parameter tuning, feature scaling, and other techniques specifically for the prediction of cardiac disease. We have used several machine learning algorithms on the Statlog (Heart) dataset that consists of clinical attributes and patients’ records across India. We perform comprehensive experiments and comparisons to evaluate the performance of each algorithm. There are a lot of metrics that we look into, and these include accuracy, precision, recall, and the f1 score for finding the best algorithm. Our system performs well and even makes excellent predictions that exceed the provided benchmark performance, which shows good results in identifying people who are really at risk of heart disease. In conclusion, the findings of our study not only highlight the potential for machine learning to revolutionize cardiovascular disease but also suggest that more research is essential in refining predictive models for optimal patient outcomes and highly individualized interventions.