Heart Disease Prediction Using Hybrid Machine Learning Algorithms

  • S.Raguvaran , R.Anandhi , A.Anbarasi, T.Megala


Cardiovascular disease is one of the most common disease in this busy world. People who are
under the age group of 50 and above are now facing so many diseases related to heart. But in today’s
world people who are in adolescence itself facing heart diseases due to the modern lifestyle foods and
much more. It is difficult to collect all the details of the patient as it holds terabytes of data’s which is
stored in the database. This paper provides the efficient machine learning and deep learning
algorithms to analyse and predict the disease as soon as possible. The dataset is collected form
Kaggle. After the dataset collection feature has to be selected. Four algorithms namely Logistic
Regression, Random Forests Classifier Algorithm, Neural network, KNN (K-Nearest Neighbour) are
used. Of these four algorithms Logistic Regression holds the best accuracy rate in predicting the
heart disease. The main objective of this paper is to provide the best accuracy rate with minimum