Hybrid Model using Machine Learning Algorithms for Heart Failure Prediction

Hybrid Model using Machine Learning Algorithms for Heart Failure Prediction

Dr. S. Bharathidason1 and C. Sujdha2

1Assistant Professor and Head, Department of Computer Science, Loyola College (Autonomous), Chennai - 600 034.

2Assistant Professor, Department of Computer Science, Mar Gregorios College of Arts & Science, Chennai - 600 037.

Corresponding author: bharathidasan@loyolacollege.edu

ABSTRACT

This model uses Python programming and machine learning algorithms to assess the detection of heart disease. The accumulation of fat has made heart disease more common and fatal in recent years. This illness is brought on by the body's extreme pressure. Several dataset factors allow us to forecast the cardiac illness. We have seen a dataset with several parameters of individual data values that is used to analyze patient performance. This model is a hybrid model that we built by combining other models to predict cardiac illness. The study's main objective is to increase the detection accuracy of heart disease by using a variety of algorithms to assess if a patient is at risk for the illness or not.

Keywords: Machine Learning, Decision Tree, Random Forest, KNN, Naïve Bayes, SVM, ANN.