The Application of Support Vector Machines and Artificial Neural Networks to the Prediction of Heart Disease

Abstract

Heart-related ailments, which are also commonly referred to as cardiovascular diseases, have been the leading cause of death throughout the world for the past few decades. They are now acknowledged as the most significant sickness in India as well as the rest of the world. It is possible to prevent the severity of the disease by receiving the appropriate care at the appropriate stage. This illness asserts that early and accurate prognosis is necessary in order to prevent causalities. Because there is a lack of effective medical care, illnesses are not being recognized at the appropriate time, and treatment cannot be initiated. It has been demonstrated that machine learning algorithms have the potential to accurately estimate the risk of heart disease based on patient data. A model for the prediction of heart disease that is based on machine learning has been provided that was developed for this study. Building a model that is based on machine learning with the purpose of providing an accurate and timely prediction of cardiac disease is the goal of this endeavor. An accuracy of 81.6% and 86.6%, respectively, has been achieved by the suggested model through the utilization of support vector machine and artificial intelligence algorithms. The results of the study indicate that the model is capable of accurately predicting the risk of acquiring heart disease with high levels of sensitivity and specificity. This provides medical practitioners with a valuable instrument that may help them identify individuals who may be more likely to acquire heart disease.