Application of the Certainty Factor Method in Diagnosing the Risk Level of Coronary Heart Disease Based on Patient Clinical Parameters
DOI:
https://doi.org/10.70211/wesw.v3i3.653Keywords:
Certainty Factor, Coronary Heart Disease, Expert System, Human–Computer Interaction, Risk CommunicationAbstract
Coronary Heart Disease (CHD) remains a global health challenge, yet health literacy gaps often make it difficult for the general public to understand clinical parameters and diagnostic uncertainty. In an effort to support social welfare and digital health accessibility, this study developed an expert system prototype that translates complex clinical data into easily understandable CHD risk levels (Low, Moderate, High). Utilizing the Certainty Factor (CF) method, the system is designed not as an independent clinical screening tool, but as a transparent, self-educational platform for non-specialist users. Evaluated on the extended UCI Heart Disease dataset (920 patient records), the CF approach successfully provided clear, explainable reasoning for its risk classifications. Although the initial algorithmic evaluation highlighted the limitations of static rules in handling the non-linear variability of medical data (achieving 46.30% accuracy), these findings emphasize that the primary value of digital health technology for the general public lies beyond mere computational accuracy. Instead, its significance is rooted in communication transparency, improving health information accessibility, and raising health awareness.
References
[1] M. F. Alhamid, “Role of Technology in Promoting Heart Healthy Behavior Change to Increase Equity in Optimal Cardiovascular Health: A Scientific Statement From the American Heart Association,” Circulation, vol. 151, no. 18, 2025. https://doi.org/10.1161/CIR.0000000000001314
[2] A. A. Alnuaim et al., “Human-computer interaction for recognizing speech emotions using multilayer perceptron classifier,” Journal of Healthcare Engineering, vol. 2022, 2022. https://doi.org/10.1155/2022/6005446
[3] K. Davis, “Toward a responsible future: recommendations for AI-enabled clinical decision support,” Digital Health, 2026. https://doi.org/10.1093/jamia/ocae209
[4] E. J. Taylor, “Outcomes of Team-Based Digital Monitoring of Patients With Hypertension and Cardiovascular Disease,” JMIR Cardio, vol. 9, e75170, 2025. https://doi.org/10.2196/75170
[5] J. Martinez et al., “Experiences of technology for increasing physical activity of older adults: digital solutions for cardiovascular health,” Journal of Medical Internet Research, 2026. https://doi.org/10.1186/s11556-025-00394-7
[6] A. Rifaiz, R. Rizky, and E. N. Susanti, “Sistem Pakar Diagnosis Penyakit Jantung Menggunakan Metode Certainty Factor Pada RSUD Berkah Pandeglang Berbasis Web,” TEKNOTIKA, vol. 5, no. 1, pp. 273–286, 2026. https://doi.org/10.30653/jurnalteknotika.v5i1.362
[7] E. T. Elmi, A. E. Budianto, and A. Aziz, “Implementasi Certainty Factor Diagnosa Penyakit Jantung Berbasis Android,” BIMASAKTI: Jurnal Riset Mahasiswa Bidang Teknologi Informasi, vol. 7, no. 2, pp. 95–99, 2025. https://doi.org/10.21067/bimasakti.v7i2.11974
[8] H. Rohayani et al., “Rancang Bangun Sistem Pakar Untuk Mendiagnosa Kerusakan Hardware Komputer dengan Metode Forward Chaining,” Journal of Informatics Management and Information Technology, vol. 3, no. 3, pp. 97–102, 2023. https://doi.org/10.47065/jimat.v3i3.248
[9] R. Rinawati, F. Handayanna, E. G. Sihombing, E. Arisawati, and M. Affarel, “Penerapan Program Sistem Pakar dalam Diagnosis Medis Penyakit Jantung Menggunakan Metode Certainty Factor,” REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer, vol. 10, no. 1, pp. 148–157, 2026. https://doi.org/10.33395/remik.v10i1.15711
[10] N. R. R. Doni and H. Zakaria, “Integrasi Fuzzy Mamdani dan Certainty Factor pada Sistem Pakar Prediksi Penyakit Jantung,” TIN: Terapan Informatika Nusantara, vol. 7, no. 3, pp. 1473–1483, 2026. https://doi.org/10.47065/tin.v7i3.10807
[11] H. Rohayani et al., “Sistem Pakar Mendiagnosa Kerusakan Smartphone Android Menggunakan Metode Certainty Factor,” Jurnal Rekayasa Sistem Informasi dan Teknologi, vol. 1, no. 3, 2024. https://doi.org/10.59407/jrsit.v1i3.484
[12] A. C. Lubis, A. Desiani, and Irmeilyana, “Analisis Metode Certainty Factor dalam Akomodasi Inexact Reasoning pada Sistem Pakar Diagnosa Penyakit Jantung,” JOINT (Journal of Information Technology), vol. 5, no. 1, pp. 11–19, 2023. https://doi.org/10.47292/joint.v5i1
[13] M. R. Fadhilah and A. Triayudi, “Analisis Perbandingan Metode Dempster Shafer dan Certainty Factor pada Sistem Pakar Untuk Mendeteksi Penyakit Jantung Koroner,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 4, pp. 2253–2261, 2024. https://doi.org/10.30865/klik.v4i4.1624
[14] D. Setiawan, A. Muhammad, and S. H. F. Dewi, “Penerapan Algoritma Klasifikasi untuk Deteksi Dini Penyakit Jantung Koroner Berdasarkan Gejala Klinis,” Teknik: Jurnal Ilmu Teknik dan Informatika, vol. 5, no. 1, pp. 18–26, 2025. https://doi.org/10.51903/teknik.v5i1.706
[15] L. D. Ajisari and P. T. Prasetyaningrum, “Sistem Pakar Diagnosa Penyakit Kardiovaskular Menggunakan Metode Certainty Factor,” Journal of Computer and Information Systems Ampera, vol. 5, no. 2, pp. 120–128, 2024. https://doi.org/10.51519/journalcisa.v5i1.471
[16] H. H. A. Rabbani, A. Jamaluddin, and A. Solehudin, “Sistem Pakar Diagnosa Awal Penyakit Jantung Menggunakan Metode Forward Chaining dan Certainty Factor Berbasis Website,” INFOTECH Journal, vol. 9, no. 2, pp. 442–451, 2023. https://doi.org/10.30865/mib.v6i1.3414
[17] Kevin, E. R. Susanto, and A. Wantoro, “Diagnosa Penyakit Jantung Menggunakan Metode Certainty Factor,” Jurnal Informatika dan Rekayasa Perangkat Lunak, vol. 3, no. 1, pp. 93–106, 2022. https://doi.org/10.33365/jatika.v3i1.1866
[18] C. Y. Gobel, M. Lasena, M. A. Puspa, and S. A. Utiarahman, “Analisis Perbandingan Metode Teorema Bayes dan Certainty Factor Pada Diagnosis Gangguan Kecemasan,” Jurnal Media Informatika Budidarma, vol. 8, no. 3, pp. 1262–1268, 2024. https://doi.org/10.30865/mib.v8i3.7683
[19] M. Hutasuhut, T. Tugiono, and A. H. Nasyuha, “Analisis Aritmia (Gangguan Irama Jantung) Menerapkan Metode Certainty Factor,” Jurnal Media Informatika Budidarma, vol. 5, no. 4, pp. 1593–1599, 2021. https://doi.org/10.30865/mib.v5i4.3289
[20] I. M. S. B. Manik, C. Rizal, and H. Kurniawan, “Analisis Akurasi dan Keandalan Metode Certainty Factor pada Sistem Pakar Diagnostik Medis,” Jurnal Multimedia dan Teknologi Informasi (Jatilima), vol. 7, no. 3, pp. 702–707, 2025. https://doi.org/10.54209/jatilima.v7i03.1560
[21] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Hoboken, NJ, USA: Pearson, 2021.
[22] Sumiati, H. Saragih, T. K. A. Rahman, and A. Triayudi, “Expert system for heart disease based on electrocardiogram data using certainty factor with multiple rule,” IAES International Journal of Artificial Intelligence, vol. 10, no. 1, pp. 43–50, 2021. https://doi.org/10.11591/ijai.v10.i1.pp43-50
[23] E. H. Shortliffe and J. J. Cimino, Biomedical Informatics: Computer Applications in Health Care and Biomedicine, 5th ed. Cham, Switzerland: Springer, 2021. https://doi.org/10.1007/978-3-030-58721-5
[24] M. M. Ali et al., “Heart disease prediction using supervised machine learning algorithms, performance analysis and comparison,” Computers in Biology and Medicine, vol. 136, p. 104672, 2021. https://doi.org/10.1016/j.compbiomed.2021.104672
[25] S. Ahmadian, S. M. J. Jalali, S. Raziani, and A. Chalechale, “An efficient cardiovascular disease detection model based on multilayer perceptron and moth-flame optimization,” Expert Systems, vol. 39, no. 1, e12914, 2022. https://doi.org/10.1111/exsy.12914
[26] R. Jahangir et al., “ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning,” BMC Cardiovascular Disorders, vol. 25, no. 260, 2025. https://doi.org/10.1186/s12872-025-04678-9
[27] N. Veldasari et al., “Analisis Perbandingan Metode Certainty Factor, Dempster Shafer dalam Deteksi Dini Gangguan Medis,” JPTI: Jurnal Pendidikan dan Teknologi Indonesia, vol. 2, no. 7, pp. 329–339, 2022. https://doi.org/10.52436/1.jpti.191
[28] P. Wagner et al., “Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery,” Computers in Biology and Medicine, vol. 176, p. 108525, 2024. https://doi.org/10.1016/j.compbiomed.2024.108525
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Aprilia Nurfazila and Hetty Rohayani
This work is licensed under a Creative Commons Attribution 4.0 International License.