چکیده مقاله
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
کلیدواژهها
نویسندگان
Omid Rahmani
M Sc Student in Industrial Engineering Majoring In Healthcare Systems, K N Toosi University;
شیوه ارجاع
Rahmani, Omid,1402,Efficient Prediction of Heart Disease Using Machine Learning Algorithms With Winsorized and Logarithmic Transformation Methods for Handling Outliers Data,The 9th International Conference on Industrial and Systems Engineering,Mashhad
ارائهشده در
مجموعه مقالات نهمین کنفرانس بین المللی مهندسی صنایع و سیستم ها21 شهریور 1402 · مشهد