Heart Disease Prediction using Machine Learning Models

Abstract

Machine learning and artificial intelligence have played an important role in the development of many disciplines, especially with the growth of knowledge in recent years. It can be more reliable and diagnose diseases faster. Therefore, machine learning algorithms are increasingly used to predict many diseases. Designs can also help us detect and identify errors to improve ad consistency and accuracy. We looked at how to diagnose heart disease using several machine learning algorithms. Heart disease is a serious disease that affects the heart. Because heart disease can be life-threatening, researchers are focusing on developing smart systems to accurately diagnose heart disease based on electronic medical records with the help of machine learning algorithms. If this information is anticipated well in advance, it can provide important insights to physicians who can tailor diagnosis and treatment to the patient's condition. The research in this report shows that it's a two-step process. The heart dataset is first prepared in the required format to run the machine learning algorithm. Medical records and other information about patients are collected through the Kaggle platform. Then use the heart disease information to determine if the patient has heart disease. Second, this article offers several useful results. Check the accuracy of machine learning algorithms such as Logistic regression, K-nearest neighbour and random forest from the confusion matrix. Current research results show that the Logistic Regression algorithm has an accuracy of up to 95% compared to other algorithms.