Implementation of Feature Selection Chi-Square to Improve the Accuracy of the Classification Model Using the Random Forest Algorithm on Coronary Artery Disease

Abstract

Coronary heart disease is a disease in which the occurrence of blockages in the blood vessels in the heart. Coronary heart disease is a fatal disease, it is better to get as much information about this disease as possible. Data Mining can classify whether a person has heart disease or not based on symptoms. Data mining builds a model that can predict whether a person has heart disease or not. How well a model performs classification can be determined from its accuracy value, but this accuracy value can still be improved. Increasing the accuracy value can be done by performing Feature Selection. The research object used in this research is a dataset about coronary heart disease obtained from the Kaggle website. The classification method used in this modeling is the Random Forest algorithm to classify whether a person has coronary heart disease or not. The Random Forest Algorithm is a classification algorithm consisting of Decision Trees for classifying. The Random Forest algorithm is used because it has been proven to produce good accuracy in several previous studies. The Feature Selection method used in this modeling is the Chi-Square hypothesis test to determine whether there is an effect of each independent variable on the dependent variable. This research compared the value of modeling accuracy without using Feature Selection with modeling using Feature Selection. The result of this study is that the model without Chi-Square Feature Selection produced an accuracy value of 96,05% and the model with Chi-Square Feature Selection produced an accuracy value of 97,33%.