Analysis of a Heart Disease Prediction System Using Machine Learning

The heart plays an important role in living organisms. Diagnosing and predicting heart diseases requires more accuracy, perfection and correctness because a small mistake can cause fatigue problems or death of a person, the cases of heart related deaths are many and the number is increasing exponentially day by day. A predictive system for disease awareness is imperative to solve this problem. Machine learning is a branch of artificial intelligence (AI), it provides prestigious support in predicting any event that takes training from natural events. In this paper, we calculate the accuracy of machine learning algorithms for heart disease prediction, for these algorithms are KNN classifier, Logistic Regression and Extra Trees Classifier using Kaggle dataset for training and testing. To implement Python programming, the best tool is Anaconda (jupyter) notebook, which has many types of libraries, header files, which make the work more accurate and precise.