Optimizing Heart Disease Prediction : A Comparative Study of Machine Learning Models Using Clinical Data
Year: 2024
Type: article
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.
Authors: Budiman Budiman, Nur Alamsyah, Elia Setiana, Valencia Claudia Jennifer Kaunang, Syahira Putri Himmaniah
Institution: Universitas Informatika dan Bisnis Indonesia
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7083837199