Heart Disease Prediction using Machine Learning Techniques

Abstract

Throughout their growth, artificial intelligence and machine learning have shown beneficial in multiple areas, particularly with the huge amount of data that has been generated recently. Making quicker and more accurate decisions regarding illness forecasts may be more dependable. The model can be used in the visualization and analysis of diseases. The article compares between different machine learning algorithms. In addition to numerous machine learning techniques, the UCI dataset is employed. Testing was done on Logistic Regression, Naïve Bayes, Support Vector Machine, Random Forest and Decision Tree algorithms. After performing the mentioned algorithm, it indicates that the decision tree performs better than another algorithm, its an accuracy of 98.54%.