Heart Disease Prediction using Logistic Regression Algorithm

Cardiovascular disease causing deaths is a big problem in this new century. Approximately one person dies each minute from heart disease. Data is produced and needs to be processed daily, owing to the rapid growth of IT. By using different algorithm combinations the information obtained is converted by data analysis into knowledge. Health professionals who are working in the field of cardiac disease have their own limitations; they can not predict the risk of high precision cardiopathy. This paper aims to enhance cardiopathy predictive performance with the Machine Learning Logistic Regression Model taking into account the health care dataset that classifies patients whether they have heart disease or not using the recorded data.