Enhancing Heart Disease Forecasting through Advanced Predictive Modeling

Heart disease is a severe public health problem that affects millions of people worldwide. Machine learning algorithms have shown potential for predicting heart disease using clinical traits like age, sex, blood pressure, and other elements. However, in order to maximize the performance associated with these algorithms, methods for selecting features are needed. A number of techniques for selecting features, including LASSO, RFE, mutual information, and the Relief algorithm, have been employed to identify the most important traits. Popular machine-learning algorithms for heart disease prediction include logistic regression, kNN, and random forest. These techniques have yielded good results, and their use can improve patient outcomes by encouraging early diagnosis and timely therapy. Overall, cardiac prognosis and control could be significantly revolutionized by making use of selected feature tackles in conjunction with machine learning algorithms.