Heart disease prediction using support vector machine

Abstract

Heart disease prediction through online consultation using machine learning refers to the application of advanced algorithms and techniques to analyze medical data collected during online consultations to predict the likelihood of an individual developing heart disease. Machine learning models are trained using historical data that includes various risk factors such as age, gender, blood pressure, cholesterol levels, and medical history. These models then utilize the input provided by patients during online consultations, such as symptoms, lifestyle habits, and additional medical tests, to generate personalized predictions about the probability of heart disease occurrence. By leveraging the power of machine learning, this approach aims to assist healthcare professionals in making more accurate diagnoses and providing timely recommendations for preventive measures or further medical intervention, ultimately improving patient outcomes and reducing the burden on healthcare systems. In this paper, a machine learning technique called Support Vector Machine (SVM) is used for heart disease prediction. Heart disease prediction through online consultation using SVM involves utilizing SVM as a machine learning algorithm to predict the likelihood of an individual having heart disease based on their consultation information provided online.