Heart Disease Prediction Using Machine Learning

Abstract

Over the last few decades, heart disease has become the leading cause of death and the most life-threatening condition on the planet. Early detection of heart disease will aid in lowering the death rate (Dinesh et al. in Prediction of cardiovascular disease using machine learning algorithms [1]). Heart disease has become one of the most difficult problems in the medical field. Machine learning is a rapidly emerging branch of research and technology that can assist people in detecting heart disease before it causes significant damage. It can determine if a person has heart disease or not by taking into account criteria such as the person’s age, cholesterol level, chest pain, and other characteristics. Our main goal is to identify the best trustworthy machine learning method that is also computationally efficient. Our main goal is to identify the best robust machine learning technique for heart disease diagnosis that is both computationally efficient and accurate. To predict and categorize patients with heart disease, we used different machine learning methods such as decision tree classifier, random forest, Naive Bayes, K-nearest neighbor, logistic regression, and support vector machine. The given model is helpful in relieving a lot of strain from determining the probability of the classifier correctly and accurately identifying heart disease. It increases medical care while lowering costs.