Estimating the Risk of Developing Heart Disease Using the Logistic Regression Model of Machine Learning

Abstract

Abstract: Asymptomatic diseases, such as cardiovascular diseases, are driving up healthcare costs to the point where they are exceeding corporate and national budgets. As a result, these diseases must be identified and treated as soon as possible. One of the hottest technologies, machine learning is used to predict diseases in many fields, including the healthcare industry. This study uses logistic regression to predict the overall risk and identify the most significant predictors of heart disease. As a result, the c predicators in this study are identified using the binary logistic model, which is one of the classification algorithms in machine learning. In addition, Jupiter Lab and Python are utilized for data analysis in order to validate the logistic regression.