Classification of Hypertension Using Naïve Bayes Method with Data Discretization Approach Risk Factors
Abstract
Generally, patients are unaware of their hypertension condition before having their blood pressure checked. One out of three Indonesians suffers from hypertension, and this figure continues to rise annually. Hypertension is often referred to as the silent killer because individuals with high blood pressure do not exhibit symptoms. This study aims to classify hypertensive patients in an effort to reduce the prevalence of hypertension in Indonesia by aiding in early detection of the disease and increasing awareness of hypertension among the Indonesian population. By using the Naïve Bayes method and implementing data discretization of risk factors, the dataset used comprises 11,627 health examination records of 4,434 participants from the Framingham Heart Study (FHS) organized by the National Institutes of Health. The classification method utilizes the Naïve Bayes Algorithm, and data discretization is performed using the CART (Classification and Regression Trees) method. The system provides an estimation of the probability of hypertension occurrence based on input factors/symptoms, where Naive Bayes achieves an accuracy rate 84.28%.
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References
Supriyono, S., & Fadila, E. (2022). Sistem pakar penegakan diagnosa penyakit hipertensi dengan inferensi forward chaining menggunakan metode Support Vector Machine (SVM). Jurnal Kesehatan Medika Udayana, 8(02), 207–221. https://doi.org/10.47859/jmu.v8i02.283
Rizky, R., Susilawati, S., Hakim, Z., & Sujai, L. (2020). Sistem Pakar Deteksi Penyakit Hipertensi Dan Upaya Pencegahannya Menggunakan Metode Naive Bayes Pada RSUD Pandeglang Banten. Jurnal Teknik Informatika Unis, 7(2), 138–144. https://doi.org/10.33592/jutis.v7i2.395
Puspa, M. A. (2018). sistem pakar diagnosa penyakit hipertensi menggunakan metode naive bayes pada rsud aloe saboe kota gorontalo. Ilkom Jurnal Ilmiah, 10(2), 166–174. https://doi.org/10.33096/ilkom.v10i2.304.166-174
Surorejo, S., Chaeriko, Y. P., Ananda, S., Studi, P., Informasi, S., Ymi Tegal, S., & Kunci, K. (2022). Penerapan Metode Naïve Bayes Pada Sistem Pakar Untuk Diagnosa Penyakit Hipertensi. 3(1), 8–17.
Sani, M. I. M., Abdullah, N. a. S., & Rosli, M. M. (2022). Review on hypertension diagnosis using expert system and wearable devices. International Journal of Power Electronics and Drive Systems, 12(3), 3166. https://doi.org/10.11591/ijece.v12i3.pp3166-3175
Santoni, M. M., Chamidah, N., & Matondang, N. (2020). Prediksi Hipertensi menggunakan Decision Tree, Naïve Bayes dan Artificial Neural Network pada 29 software KNIME. Techno.Com, 19(4), 353–363. https://doi.org/10.33633/tc.v19i4.3872
Nugroho, W. E., Prihandoyo, T., & Somantri, O. (2022). Optimalisasi Metode Naive Bayes untuk Menentukan Program Studi bagi Calon Mahasiswa Baru dengan Pendekatan Unsupervised Discretization. Infotekmesin, 13(1), 161– 167. https://doi.org/10.35970/infotekmesin.v13i1.1048
Changpetch, P., Pitpeng, A., Hiriote, S., & Yuangyai, C. (2021). Integrating Data mining Techniques for Naïve Bayes Classification: Applications to medical datasets. Computation (Basel), 9(9), 99. https://doi.org/10.3390/computation9090099
Yanti Apriyani, Iskandar, I. D. I., Mira Kusmira, Melisa Winda Pertiwi, Imam Amirulloh, & Taufik Wibisono. (2021). Implementasi Sistem Pakar dengan Algortima Naïve Bayes dengan Laplace Correction untuk Diagnosis Tuberkulosis Paru. INFORMASI (Jurnal Informatika Dan Sistem Informasi), 13(1), 24–46. https://doi.org/10.37424/informasi.v13i1.72
Nurlelah, E., Abdilah, A., & Ghani, M. A. (2019). implementasi algoritma naive bayes pada sistem pakar untuk diagnosis penyakit demam berdarah dengue berbasis website. Repository Universitas Bina Sarana Informatika (RUBSI), 1–8. https://repository.bsi.ac.id/index.php/unduh/item/311052/implementasialgoritma-naive-bayes-pada-sistem-pakar-untuk-diagnosis-penyakit-demamberdarah-dengue-berbasis-website.pdf
Haffandi, M. Y., Haerani, E., Syafria, F., & Oktavia, L. (2022). klasifikasi penyakit paru-paru dengan menggunakan metode naïve bayes classifier. Jurnal Teknik Informasi Dan Komputer, 5(2), 176. https://doi.org/10.37600/tekinkom.v5i2.649
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). SciKit-Learn: Machine Learning in Python. HAL (Le Centre Pour La Communication Scientifique Directe). https://hal.inria.fr/hal-00650905
K. A. Khalim, U. Hayati, and A. Bahtiar, “Perbandingan Prediksi Penyakit Hipertensi Menggunakan Metode Random Forest Dan Naïve Bayes,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 1, pp. 498–504, Mar. 2023, doi: 10.36040/jati.v7i1.6376
S. Misra and H. Li, “Noninvasive fracture characterization based on the classification of sonic wave travel times,” in Elsevier eBooks, 2020, pp. 243–287. doi: 10.1016/b978-0-12-817736-5.00009-0.
S. Krishnan, “Machine learning for biomedical signal analysis,” in Elsevier eBooks, 2021, pp. 223–264. doi: 10.1016/b978-0-12-813086-5.00006-2.
D. Maslove, T. Podchiyska, and H. J. Lowe, “Discretization of continuous features in clinical datasets,” Journal of the American Medical Informatics Association, vol. 20, no. 3, pp. 544–553, May 2013, doi: 10.1136/amiajnl-2012-000929.
J. Du et al., “Developing a hypertension visualization risk prediction system utilizing machine learning and health check-up data,” Scientific Reports, vol. 13, no. 1, Nov. 2023, doi: 10.1038/s41598-023-46281-y
Fitri, D. R, "Diagnose Enforcement And Treatment Of High Blood Pressure".Jurnal Majority, Vol 4,no 3,https://juke.kedokteran.unila.ac.id/index.php/majority/article/download/549/550
W. Setiawan and S. Ratnasari, “Sistem Pakar Diagnosis Penyakit Mata Menggunakan Naïvebayes Classifier,” Setiawan | Prosiding Semnastek, Nov. 12, 2014. https://jurnal.umj.ac.id/index.php/semnastek/article/view/345
A. Sabrani, W. Wedashwara, and F. Bimantoro, “Multinomial Naïve Bayes untuk Klasifikasi Artikel Online tentang Gempa di Indonesia,” Jurnal Teknologi Informasi, Komputer Dan Aplikasinya, vol. 2, no. 1, pp. 89–100, Mar. 2020, doi: 10.29303/jtika.v2i1.87.
Y. E. Fadrial, “Algoritma Naive Bayes untuk mencari perkiraan waktu studi mahasiswa,” Intecoms, vol. 4, no. 1, pp. 20–29, May 2021, doi: 10.31539/intecoms.v4i1.2219.
A. Sabrani, W. Wedashwara, and F. Bimantoro, “Multinomial Naïve Bayes untuk Klasifikasi Artikel Online tentang Gempa di Indonesia,” Jurnal Teknologi Informasi, Komputer Dan Aplikasinya, vol. 2, no. 1, pp. 89–100, Mar. 2020, doi: 10.29303/jtika.v2i1.87.
