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 97.95%. ‎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.‎