Large number of patients related data is stored and maintained in
the health industry. Heart disease is the most common one
nowadays. The different ways of predicting it are Electrocardiogram
(ECG), stress test, and Heart MRI. Here, the proposed model uses
13 parameters for the prediction of heart disease that includes
heart rate, chest pain, cholesterol level, blood pressure, Age etc.
The aim of this model is to predict whether heart disease is present
or not using the various machine learning models such as Decision
Tree, Random Forest, Logistic Regression, Naïve Bayes. We have
achieved 0.3312 log loss using the Logistic Regression.