A ROBUST PREPROCESSING AND FEATURE SELECTION FRAMEWORK IS PROPOSED TO ENHANCE HEART DISEASE PREDICTION ACCURACY

Abstract

Heart disease is now the leading health issue in the world and requires proper measures to diagnose and prevent heart disease at an early stage. The paper introduces statistical and machine learning methods for forecasting heart disease by analyzing vital health indicators and lifestyle factors. To create a predictive framework, the University of California, Irvine (UCI) Heart Disease Dataset, comprising patient-specific characteristics, is used. The performance of three classification models, including the Logistic Regression, K-Nearest Neighbors (KNN), and the Random Forest, is compared in terms of their predictive performance. The research methodology can be divided into two stages: identification of the most important clinical characteristics that suggest cardiovascular risk; evaluation of the accuracy of the model on the data. The results indicate that machine learning and data mining tools can be used to diagnose and prevent cardiovascular diseases promptly.