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All Journal JUSIFO : Jurnal Sistem Informasi
Eppi Kriawati Harefa
Department of Information System, Universitas Prima Indonesia

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Support Vector Machine for Classifying Heart Failure, Hypertension, and Normal Heart Condition Surya Amando Bangun; Elvis Sastra Ompusunggu; Wilson Wilson; Eppi Kriawati Harefa
JUSIFO : Jurnal Sistem Informasi Vol 11 No 1 (2025): JUSIFO (Jurnal Sistem Informasi) | June 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i1.28113

Abstract

Cardiovascular diseases, particularly heart failure and hypertension, remain among the leading causes of global mortality, underscoring the urgent need for accurate early diagnosis. This study proposes a classification model based on the Support Vector Machine (SVM) algorithm to distinguish among heart failure, hypertension, and normal heart conditions using real-world clinical data. The dataset was preprocessed through normalization and nominal-to-numerical conversion and validated by medical experts to ensure data quality. K-Fold Cross Validation (K=10) was employed to ensure model robustness and mitigate overfitting. The SVM classifier utilized a linear kernel and achieved high performance in terms of accuracy, precision, and recall. The results demonstrate the effectiveness of the proposed model in classifying multiple cardiovascular conditions with clinically relevant input features. This research contributes to the advancement of intelligent diagnostic tools and supports the integration of machine learning into clinical decision-making processes.
Co-Authors Elvis Sastra Ompusunggu Surya Amando Bangun Wilson Wilson