Efficient Prediction of Heart Disease Using Machine Learning Algorithms With Winsorized and Logarithmic Transformation Methods for Handling Outliers Data

Heart disease is a prevalent and life-threatening condition that poses significant challenges to healthcare systems worldwide. Accurate and timely diagnosis of heart disease is crucial for effective treatment and patient management. In recent years, machine learning algorithms have emerged as powerful tools for predicting and identifying individuals at risk of heart disease. This article highlights the importance of heart disease diagnosis and explores the potential of machine learning algorithms in enhancing the diagnosis of heart disease accuracy. This article presents a study to develop a model for predicting heart disease in the Cleveland patient dataset. The innovation of this research involved identifying and handling outliers data using Winsorized and Logarithmic transformation methods. We also used Wrapper and Embedded methods to determine the most critical features for diagnosing heart disease. In addition to the usual features, Exercise-induced angina and No. of major vessels were found to be important. We then compared the performance of four machine learning algorithms, including KNN, Naïve Bayes' Classifier, Decision Tree, and Support Vector Classifier to determine the best algorithm for predicting heart disease. The findings showed that the Decision Tree algorithm had the best performance with an accuracy of ۹۷.۹۵%. Overall, this study provides insights into developing an accurate model for predicting heart disease, which could help improve the diagnosis and treatment of this condition.

Keywords:

Heart disease , KNN , Naïve Bayes' Classifier , Decision Tree , Support Vector Classifier , Winsorized and Logarithmic transformation methods , Wrapper and Embedded methods