📅 12 December 2024
DOI: 10.62951/ijsme.v1i4.96
Optimizing Heart Disease Prediction : A Comparative Study of Machine Learning Models Using Clinical Data
International Journal of Science and Mathematics Education
Asosiasi Riset Ilmu Matematika dan Sains Indonesia (ARIMSI)
📄 Abstract
Cardiovascular disease is a leading cause of death globally, necessitating effective predictive systems. This research aims to analyze the effectiveness of various machine learning (ML) models—Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN)—in predicting heart disease using publicly available health data. The study involved pre-processing data, training models, and evaluating them using accuracy, precision, recall, F1-score, and G-Mean metrics. The results show that KNN is the most reliable model, with the highest accuracy of 92%. Significant health features were identified, such as chest pain type and maximum heart rate. The study contributes to improving clinical decision support systems by identifying optimal ML models for heart disease prediction.
🔖 Keywords
#Heart disease prediction; machine learning; Logistic Regression; K-Nearest Neighbors; health data
ℹ️ Informasi Publikasi
Tanggal Publikasi
12 December 2024
Volume / Nomor / Tahun
Volume 1, Nomor 4, Tahun 2024
📝 HOW TO CITE
Budiman Budiman; Nur Alamsyah; Elia Setiana; Valencia Claudia Jennifer Kaunang; Syahira Putri Himmaniah, "Optimizing Heart Disease Prediction : A Comparative Study of Machine Learning Models Using Clinical Data," International Journal of Science and Mathematics Education, vol. 1, no. 4, Dec. 2024.
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