Cardiac Disease Analysis Using Machine Learning Technique

Abstract

One of the most well-known uses of artificial intelligence, and machine learning (ML), is revolutionizing the field of healthcare. In this work, the use of machine learning to determine a person's risk of heart attack is studied. Cardiovascular diseases (CVDs) are common and can possibly be fatal for people anywhere in the globe. A person's age, cholesterol level, chest discomfort, and other characteristics may all be taken into account using machine learning to determine if they have a cardiovascular disease. Cardiovascular disease diagnosis can be facilitated by machine learning classification algorithms based on supervised learning. This study explores the effectiveness of classification approach developed using Artificial Neural Network (ANN), and compares it performance with Random Forest Classifier in predicting the chance of heart attack for a person. Different biological and biochemistry profile were used as clue for classifying whether a patient will suffer from cardiac arrest or not.