A Review on Machine Learning Techniques for Data-Driven Heart Disease Prediction
Machine learning algorithms are part of Artificial Intelligence (AI) and the emerging data science field. They are used in solving different real world problems. Heart disease prediction is no exception. In the literature, different algorithms are found suitable for prediction of heart disease. However, they are data-driven approaches. Feature extraction, feature selection and feature optimization are important for improving classification algorithms. Classification algorithms are able to perform prediction task based on the training provided to them. Hence they are known as supervised machine learning algorithms. In this paper, we discuss different aspects related to machine learning used for heart disease prediction. It throws light into methods that improve the classification performance as well. Such methods are known as feature selection methods. With such methods, the performance of ML algorithms is boosted. There are feature optimization methods as well as discussed in this paper. With all these methods, this paper provides useful insights to academia and industry with regard to heart disease prediction research.