WITHDRAWN: A comparative study on machine learning based heart disease prediction

Abstract

Over the last few decades, the population worldwide is suffering from heart disease, which is considered one of the most significant fatalities. About 17.7 million people die on average every year because of heart disease, the World Health Organization (WHO). There will be many difficulties in the prognosis of heart disease due to various risk factors like diabetes, high blood pressure, high cholesterol, abnormal pulse rate, and many other factors. The main goal is to save humans' lives by detecting abnormalities in heart conditions, which would be achieved by identifying and processing raw data collected based on heart information. The healthcare industry has found that Machine Learning (ML) is a useful and accurate decision-making technique in the data collection produced in large quantities. The medical decision support systems developed were effective based on the software and the different algorithms proposed by many researchers. Here a study is done based on the various techniques using the different algorithms and their performance analysis. The predicting model was introduced with several combined features, and among the multiple methods and were other classification techniques. Many existing ways discussed, among which the accuracy level was found as 88.7% using the Hybrid Random Forest with a Linear Model (HRFLM) technique.