Performance of the Decision Tree Model for Data Prediction Using a Heart Disease Prediction Dataset

Abstract

Heart disease is one of the leading causes of death worldwide, and its incidence continues to rise. This condition significantly impacts patients' quality of life as well as the public healthcare system. Therefore, developing predictive models for heart disease using machine learning techniques, such as Decision Trees, has become an essential approach for rapid and accurate diagnosis.

The objectives of this research are: (1) to develop a Decision Tree model for predicting heart disease, and (2) to evaluate the performance of the Decision Tree model. The research was conducted following the fundamental five-step data science process, which includes data collection, data exploration, data preparation, model selection, and model performance evaluation.

          The research findings revealed that the performance of the Decision Tree model for heart disease prediction demonstrated high effectiveness. The model achieved an accuracy of 98.54%, a precision of 100%, a recall of 97.09%, and an F1-score of 98.52%.