Penerapan Machine Learning untuk memprediksi Resiko Pengidap Penyakit Jantung menggunakan Algoritma decision tree

Abstract

Heart disease remains a leading cause of mortality worldwide, necessitating innovative approaches to improve diagnosis and management. This study aims to enhance the prediction of heart disease risk using machine learning, particularly the Decision Tree algorithm. A publicly available dataset containing 303 entries with 14 features related to heart disease risk factors, such as age, cholesterol levels, blood pressure, and electrocardiogram results, was utilized. The data underwent preprocessing steps, including normalization, handling outliers, and standardization, to ensure optimal model performance. The Decision Tree algorithm was trained on 80% of the dataset and evaluated on the remaining 20%. The model achieved an accuracy of 80%, with a balanced F1-score of 0.82, demonstrating its effectiveness in predicting heart disease risk. Feature importance analysis revealed that cholesterol levels, age, and resting blood pressure were the most influential predictors. The Decision Tree's interpretability provides valuable insights for medical practitioners, enabling more accurate and transparent risk assessments. This study highlights the potential of machine learning in medical diagnostics, particularly in identifying high-risk individuals for early intervention and better patient outcomes.