Heart Disease Classification/Prediction: A Review

Abstract

Heart disease is the biggest cause of death worldwide; it cannot be seen with the bare eyes and occurs suddenly when its limits are reached. It requires a correct diagnosis at the right moment. Every day, the health care sector generates a massive amount of data about patients and diseases. However, scholars and practitioners do not make effective use of this data. The healthcare sector is currently data-rich but knowledge-poor. To effectively extract information from databases and apply that knowledge for diagnosis that is even more precise and decision-making, a variety of data mining and machine learning approaches and technologies are available. As research on algorithms for predicting heart disease expands, it is critical to assess the findings, which are now unclear. The primary purpose of this research paper is to present a summary of current research on the use of datasets, classifiers, data preprocessing methods, and the efficiency of integrating both to predict heart disease, with comparison findings and analytical conclusions. According to the study, the performance of the heart disease prediction system is improved in many scenarios by the use of KNN, ANN, RF, PCA, χ 2 and GA algorithms.