Abstract:
currently heart disease is considered among top major causes of deaths in the globe, prediction of heart disease is a serious complexity in medical data processing. Machine learning (ML) has proven the beneficial in assisting with decision-making and prediction from the massive amounts of data provided by health care industry. We found machine learning approaches being employed in recent advancements in a long list of Internet Of Things (IOT) in a variety of industries. Different research suggests merely a glimmer of hope for using ML algorithms to predict cardiac disease. Several machine learning approaches are used in this paper to compare and analyze the outcomes of the UCI dataset using different machine Learning algorithms the data was collected by researchers from the University of California Irvine It contains 75 column and will use only 14 features. Calculating the accuracy and confusion matrix. some encouraging results are achieved and validated. the dataset consists various non - relevant attributes that were handled, and normalized for improved returns.
