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All Journal Zero : Jurnal Sains, Matematika, dan Terapan
Simatupang, Septian
Politeknik Wilmar Bisnis Indonesia
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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Prediction of Heart Disease Risk Based on Patient Health History Using the Support Vector Machine (SVM) Algorithm Simatupang, Septian; Ramadhansyah, Rizki; Tumanggor, Rustianna; Tan, Eric Pratama; Fajar, Syafrizal Amri
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i2.26087

Abstract

Heart disease remains the leading cause of death worldwide, with early detection being critical to improving patient outcomes. This study develops a heart disease risk prediction model using the Support Vector Machine (SVM) algorithm. A dataset of 303 patient records with 14 clinical attributes was used, including age, blood pressure, cholesterol, and chest pain type. Data preprocessing, normalization, and feature selection were performed to optimize the model. Evaluation metrics such as accuracy (92%), precision (90%), recall (96%), and F1-score (93%) demonstrated significant improvements over the baseline model. These results highlight the SVM model’s effectiveness as a tool for early heart disease detection, offering potential for enhanced predictive healthcare, particularly in Indonesian clinical settings. 
Co-Authors Fajar, Syafrizal Amri Ramadhansyah, Rizki Tan, Eric Pratama Tumanggor, Rustianna