One leading cause of death is heart failure CHD and CAD
Algorithms that incorporate the evaluation of clinical biomarkers with risk factors can assist physicians in predicting CHD and make clinical decision making easier and reliable
Machine Learning (ML) is a process used to interpret datasets by
using computers that acquire knowledge from experiences.
Techniques used include support vector machines, neural networks, decision trees, rules based, fuzzy logic, k nearest neighbor random forest and ensemble
-The most promising techniques seem to be ensembles. Ensembles use multiple ML techniques to get better results. Some examples of the most accurate ensembles seem to be Genetic algorithm with SVM, genetic algorithm with neural networks and ensemble of bagged decision trees.
Since diagnosing heart disease is a matter of life or death, it must be done in a timely manner. A timely diagnosis is a crucial part of avoiding a death. Along with timeliness, accuracy plays a major role in diagnosis. Feature selection and reduction techniques increase the accuracy of the algorithms.
https://dl.acm.org/doi/10.1145/3318299.3318343