Coronary Illness Prediction Using Random Forest Classifier Rekha G a,1 , Shanthini B a and Ranjith Kumar V b a Department of CSE, St. Peter’s Institute of Higher Education & Research, Chennai, TN, India b Department of Mechanical Engineering, Sri Sairam Engineering College, Chennai, TN, India Abstract. Heart diseases or Cardiovascular Diseases (CVDs) are the main cause of death on the planet throughout the most recent years and become the most dangerous disease in India and the entire world. The UCI repository is utilized to calculate the exactness of the AI calculations for foreseeing coronary illness, as k- nearest neighbor, decision tree, linear regression, and support vector machine. Different indications like chest pain, fasting of heartbeat, etc., are referenced. Large datasets, which are not available in medical and clinical research, are required in order to apply deep learning techniques. Surrogate data is generated from Cleveland dataset. The predicted results show that there is an improvement in classification accuracy. Heart disease is one of the most challenging diseases to diagnose as it is the most recognized killer in the present day. Utilizing AI algorithms, this paper gives anticipating coronary illness. Here, we will use the various machine learning algorithms such as Support Vector Machine, Random Forest, KNN, Naive Bayes, Decision Tree and LR. Keywords. Coronary artery disease, Decision tree, K nearest neighbor; SVC, Logistic Regression, Naïve Bayes, Accuracy 1. Introduction Cardiovascular disease is the most recognized killer in the present world. Consistently an excessive number of individuals are kicking the bucket because of heart illness. CAD can emerge because of lacking blood supply to courses. The two most common cardiac emergencies are a heart attack and myocardial infarction. Heart disease describes a group of conditions that affect heart. Heart diseases include:  Arrhythmias  Congenital Heart Defects  Heart valve disease The aim of this study is to achieve accuracy so that it can predict a heart attack. Ages, sex, blood pressure, cholesterol, chest pain, heart rate, and so on are risk factors. 1 G.Rekha, Department of CSE, St. Peter’s Institute of Higher Education & Research, Chennai, TN, India; Email: rekhabensy@gmail.com. Recent Trends in Intensive Computing M. Rajesh et al. (Eds.) © 2021 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/APC210285 812