Survey on Heart Disease Prediction Using Machine Learning Techniques

In this decade, heart disease (HD) commonly referred as cardiovascular disease (CVD) becomes the major cause of mortality globally. It links a slew of risk factors for heart disease with an urgent need for precise, dependable, and practical methods for making an early diagnosis as well as managing the disease. In the healthcare industry, data mining is just a typical approach for analyzing large count of data. They use a variety of machine learning (ML) and data mining approaches to examine the large count of complicated medical data, assisting doctors in the prediction of HD. The goal of this survey is to conduct a review on 25 papers contributed toward HD prediction via ML models. Moreover, the review analyzed the diverse ML models used for prediction purpose. Further, it reviews and analyzes the features that are intake for predicting the disease. Subsequently, the comprehensive study in each contribution offers the performance attainments. Moreover, the analytical review in certain contributions reveals the highest performance attainments. In addition, the various tools used in the reviewed papers are also examined. At last, the survey expands with different research gaps and its issues which are helpful for the researchers to encourage enhanced future works on HD prediction via ML models.