Optimasi Performa Prediksi Penyakit Jantung Menggunakan Teknik Stacking Classifier
- Eka Amelya Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
-
Erliyan Redy Susanto
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
(*) Corresponding Author
Abstract
Cardiovascular diseases, including heart disease, are among the leading causes of death in Indonesia. Heart disease is a condition that disrupts the function of the heart and blood vessels, often caused by blockages or narrowing of the arteries. Arteries play a crucial role in delivering oxygen-rich blood from the heart to the entire body, including the heart muscles through the coronary arteries. This condition can result from various factors such as vascular blockages, inflammation, infections, or congenital abnormalities. Such issues can impair the heart's ability to pump blood efficiently, posing a serious threat to an individual's health. This study aims to improve the accuracy of heart disease prediction by implementing the stacking classifier technique—an ensemble learning method that combines multiple machine learning algorithms, namely Support Vector Machine (SVM), Logistic Regression, and Decision Tree. The dataset used has undergone a standardization process and has been validated using the stratified k-fold cross-validation method to ensure stable predictive results. The primary contribution of this research lies in enhancing the accuracy and efficiency of heart disease diagnosis through the application of the stacking classifier, which effectively handles complex and imbalanced datasets. Previous studies have utilized the SMOTEEN technique for heart disease prediction. However, the findings of this study demonstrate that the stacking classifier approach performs better. Evaluation results show that this method achieves an accuracy of 88.52%, precision of 87.88%, recall of 90.62%, and an ROC-AUC of 94.18%, proving its effectiveness in improving medical diagnosis reliability and reducing prediction errors that could pose risks in the healthcare field.
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