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Section Computer Science

Comparison of Logistic Regression and Support Vector Machine Algorithm Performance in Heart Failure Prediction

Perbandingan Kinerja Algoritma Logistic Regression dan Support Vector Machine dalam Prediksi Gagal Jantung
Vol. 10 No. 2 (2025): December:
DOI : https://doi.org/10.21070/acopen.10.2025.11682
Published : Jul 22, 2025

Afrahul Hidayah Siregar (1), Saut Dohot Siregar (2)

(1) Program Studi Teknik Informatika, Universitas Prima Indonesia, Indonesia
(2) Program Studi Teknik Informatika, Universitas Prima Indonesia, Indonesia
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Abstract:

General Background: Heart failure remains a global health concern due to its high prevalence and mortality rates. Specific Background: In Indonesia, heart disease ranks as a leading cause of death, emphasizing the need for reliable predictive tools. Knowledge Gap: Despite the availability of machine learning algorithms, limited studies provide a comparative evaluation using updated clinical datasets in the Indonesian context. Aims: This study aims to compare the performance of Logistic Regression and Support Vector Machine (SVM) in predicting heart failure. Results: Using a dataset of 918 samples and rigorous preprocessing, SVM achieved 90% accuracy with an AUC of 0.93, outperforming Logistic Regression, which scored 88% accuracy and an AUC of 0.9304. SVM demonstrated superior sensitivity and robustness in handling non-linear data, while Logistic Regression offered better calibration for risk interpretation. Novelty: The study’s novelty lies in its integrated open-source pipeline, use of biomedical signal features, and statistical validation via McNemar’s test. Implications: These findings support the implementation of SVM in automated clinical decision systems for early heart failure detection, while highlighting Logistic Regression's value in interpretability-focused clinical settings.
Highlight :



  • SVM has higher accuracy and F1-score, indicating superior classification performance.

  • Logistic Regression remains superior in probability calibration, important for clinical risk interpretation.

  • Both models effectively support the development of machine learning-based health prediction systems.


Keywords : Logistic Regression, Support Vector Machine (SVM), Heart Failure, Machine Learning Classification, Prediction Calibration

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