A Comprehension Analysis of Heart Disease Recognition using Machine Learning

Cardiovascular disease (CVD) is a challenging condition to manage, impacting a significant global population. Within the realm of cardiology, rapid and accurate identification of heart disease is imperative for effective intervention. As a result of the availability of data with varied features, many feature determination algorithms are available in textual form. The chosen subset of characteristics must be continuous to be used in practical applications. This study suggests a sequential feature selection strategy for identifying fatal events in heart disease patients while they are receiving therapy and choosing the most crucial traits. There are several machine learning algorithms employed, including, RF, LR, DT, SVM, ANN, and KNN for heart disease recognition. The HD framework performed significantly better than the traditional CVD risk score. The Machine Learning paradigm is effective for HD prediction in individuals with medium to high cardiovascular risk.