Prediction of heart attack risk using linear discriminant analysis methods

Abstract

Aims: The interruption of blood flow to the heart muscle is called a heart attack. During a heart attack, the risk of permanent damage increases with every second the heart tissue cannot receive enough blood. If early and appropriate intervention is not performed, loss of heart tissue occurs. Causes such as smoking, cholesterol, diabetes, high blood pressure, old age, obesity, genetics, and high levels of certain substances produced in the liver are the main risk factors for heart attack. This study aims to predict the risk of heart attack with machine learning methods using a dataset created by considering risk factors. Methods: The performances of three types of Linear Discriminant Analysis classifiers, Normal, Ledoit-Wolf, and Oracle Shrinkage Approximating, were compared on the Cleveland dataset. Results: Normal Linear Discriminant Analysis made the best classification with 83.60% accuracy and performed better than regularized versions. Conclusion: Linear Discriminant Analysis methods are a promising classifier for heart attack prediction and can be applied in hospitals as an objective and automated system that eases specialists' workload and helps reduce diagnostic costs.