Performance of different Machine Learning Techniques for the Prediction of Heart Diseases

Abstract

Machine Learning (ML), which is the most well known sub area Artificial Intelligence, is revolutionizing the area of research. ML is primarily an subset of Artificial Intelligence(AI) that has been a crucial component of digitalization solutions that has gotten a lot of buzz in the digital world. In this work, ML is utilized to determine whether or not a person has cardiac disease. ML may be used to determine if a person has a cardiovascular illness based on particular characteristics such as chest discomfort, cholesterol levels, age, and other factors. Cardiovascular disease diagnosis can be simplified using ML classification algorithms that are based on supervised learning. To distinguish those with cardiac illness from those who do not, many ML algorithms are being used including Naïve Bayes Classifier, Logistic Regression Classifier and Random Forest.. The dataset contains certain irrelevant features that are removed during the data cleaning stage, and the data is also standardized for better results. The results and analyses of the publicly available ML Heart Disease dataset are being compared in this paper using different ML methods. The accuracy as well as confusion matrix are also being used to validate a good amount of promising outcomes.

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