Comparison of Models for Predicting Outcomes in Patients for Heart Disease
DOI:
https://doi.org/10.54097/ywe6tj15Keywords:
Heart disease; Logistic regression; Decision tree; Random forest.Abstract
Over one quarter of all global deaths are caused by heart disease. Heart disease takes the lives of nineteen million people every year, making it the major cause of death. The risks of cardiovascular disease have been steadily rising across the globe over the past thirty years and COVID-19 has only exacerbated the predicament. Curing heart disease is a very expensive task, and thus a prediction is necessary. This study seeks to review the accuracy and accessibility of three models that are commonly used to predict decisions, which are Logistic Regression, Decision Tree, and Random Forest. Each method is used to fit models using data with 70000 cases and eleven variables. The accuracy rates for the Logistic Regression, Decision Tree, and Random Forest are 0.54, 0.56, and 0.51, respectively. Through a comparison, the Random Forest model is superior to the other models with the smallest misclassification error. Nevertheless, it does not have the accessibility that the other two regressions possess.
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