Prediction of Coronary Artery Disease Using Genetic Algorithm Based Feature Selection and Random Forest Classifier
نویسنده
چکیده
Coronary Artery Disease (CAD) is one of the most prevalent diseases, which can lead to disability and sometimes even death. Diagnostic procedures of CAD are typically invasive, although they do not satisfy the required accuracy. Hence machine learning methods can be used, so that diagnosis can be made faster and with improved accuracy. There are many features that need to be taken into consideration for any disease prediction, which increases the processing time. Hence feature selection mechanisms can be used to reduce the number of features and then the diagnosis can be made. The first step involves feature selection done using Genetic Algorithm (GA) and the second step involves classification which is done using Random Forest (RF) classifier. Keywords–Coronary Artery Disease, Genetic Algorithm, Machine Learning, Random Forest Classifier
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