LVQ Algorithm for The Classification of Hypertension Based on ESH Guideline
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Published:
Nov 1, 2020
Keywords:
Hypertension, ESH Guideline, Learning Vector Quantization, Machine Learning, Accuracy
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Computer Science
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Abstract
Hypertension was a global health problem, including Indonesia, that increases mortality, morbidity, and cost. In Indonesia, hypertension kept on increasing due to change in lifestyle, consumptions of food with a high level of fat, cholesterol, less physical activity, and a high level of stress, etc. One of the classifications of hypertension used in some country were European Society of Hypertension (ESH) guideline. Learning Vector Quantization (LVQ) was a method in machine learning for classifying data. LVQ were often used in pattern recognition processes such as images, sounds, etc. The purpose of this study was to see an increase in accuracy of hypertension classification based on ESH guideline as weight data. In this study, hypertension classification based on ESH guideline was used as weight data with LVQ method and the parameters used were 2 features, 100 epochs, 0.05 learning rate, 0.01 reducing factor, train data 70%, validation data 30%, and test data 30% from total data used. The result obtained in this study were 94.6667% in the hypertension classification process based on ESH guideline using LVQ method. The conclusion of this study, there was an increase in the accuracy of hypertension classification based on ESH guideline using the LVQ method.
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Lase, E., Wonderson, W., Christensen, C., Afrianti, D., Permana, A. D. and Abdi Dharma, A. (2020) “LVQ Algorithm for The Classification of Hypertension Based on ESH Guideline”, Jurnal Mantik, 4(3), pp. 1772-1778. doi: 10.35335/mantik.Vol4.2020.1006.pp1772-1778.
References
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[25] E. B. Budianita, “Penerapan Metode Learning Vector Quantization2 (LVQ 2) Untuk Menentukan Gangguan Kehamilan Trimester I,” J. Sains dan Teknol. Ind., vol. 15, no. 2, p. 144, Jun. 2018.
[2] A. A. Lukito, E. Harmeiwaty, and N. M. Hustrini, Konsensus Penatalaksanaan Hipertensi 2019, vol. 36, no. 6. 2019.
[3] I. Agustinus, E. Santoso, and B. Rahayudi, “Klasifikasi Risiko Hipertensi Menggunakan Metode Learning Vector Quantization ( LVQ ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 8, pp. 2947–2955, 2018.
[4] S. Sakr et al., “Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project,” PLoS One, vol. 13, no. 4, p. e0195344, Apr. 2018.
[5] M. R. Andriansyah, E. Santoso, and Sutrisno, “Klasifikasi Risiko Hipertensi Menggunakan Fuzzy Decision Tree Iterative Dichotomiser 3 (ID3),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. Vol 2 No 12 (2018), pp. 7088–7096, 2018.
[6] A. W. Rahmadani, A. I. Jaya, and N. Nacong, “PREDIKSI PENYAKIT TUBERCULOSIS PARU (TB PARU) MENGGUNAKAN METODE LEARNING VEKTOR QUANTIZATION (LVQ),” J. Ilm. Mat. DAN Terap., vol. 15, no. 1, pp. 20–27, May 2018.
[7] W. A. Setyowati and W. F. Mahmudy, “Optimasi Vektor Bobot Pada Learning Vector Quantization Menggunakan Particle Swarm Optimization Untuk Klasifikasi Jenis Attention Deficit,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. May, pp. 4428–4437, 2018.
[8] Z. A. Leleury, Y. A. Lesnussa, and J. Madiuw, “Sistem Diagnosa Penyakit Dalam dengan Menggunakan Jaringan Saraf Tiruan Metode Backpropagation dan Learning Vector Quantization,” J. Mat. Integr., vol. 12, no. 2, p. 89, Jul. 2017.
[9] S. Agustin, B. D. Setiawan, and M. A. Fauzi, “Klasifikasi Berat Badan Lahir Rendah (BBLR) Pada Bayi Dengan Metode Learning Vector Quantization (LVQ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput. e-ISSN, vol. 2548, no. 3, p. 964X, 2018.
[10] C. F. Virkhansa, B. D. Setiawan, and C. Dewi, “Klasifikasi Status Gunung Berapi dengan Metode Learning Vector Quantization ( LVQ ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 7, pp. 7119–7126, 2019.
[11] S. Ramzini, D. E. Ratnawati, and S. Anam, “Penerapan Metode Learning Vector Quantization ( LVQ ) untuk Klasifikasi Fungsi Senyawa Aktif Menggunakan Notasi Simplified Molecular Input Line System ( SMILES ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 12, pp. 6160–6168, 2018.
[12] J.-H. Wu et al., “Risk Assessment of Hypertension in Steel Workers Based on LVQ and Fisher-SVM Deep Excavation,” IEEE Access, vol. 7, pp. 23109–23119, 2019.
[13] T. J. Bihl, T. J. Paciencia, K. W. Bauer, and M. A. Temple, “Cyber-Physical Security with RF Fingerprint Classification through Distance Measure Extensions of Generalized Relevance Learning Vector Quantization,” Secur. Commun. Networks, vol. 2020, no. 1, pp. 1–12, Feb. 2020.
[14] R. Hamidi, M. T. Furqon, and B. Rahayudi, “Implementasi Learning Vector Quantization ( LVQ ) untuk Klasifikasi Kualitas Air Sungai,” J-Ptiik, vol. 1, no. 12, pp. 1758–1763, 2017.
[15] E. B. Ladauw, D. E. Ratnawati, and A. A. Supianto, “Identifikasi Penyakit Mata Menggunakan Metode Learning Vector Quantization ( LVQ ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 12, pp. 6989–6996, 2018.
[16] A. C. Joseph, M. S. Karthik, R. Sivasakthi, R. Venkatanarayanan, and J. Sam Johnson Udaya Chander, “JNC 8 versus JNC 7 – Understanding the evidences,” Int. J. Pharm. Sci. Rev. Res., vol. 36, no. 1, pp. 38–43, 2016.
[17] D. Irawan, I. Muhimmah, and T. Yuwono, “PROTOTYPE SMART INSTRUMENT UNTUK KLASIFIKASI PENYAKIT HIPERTENSI BERDASARKAN JNC-7,” J. Teknol. Inf. dan Terap., vol. 4, no. 2, pp. 111–118, Apr. 2019.
[18] N. Azwanti and E. Elisa, “Analisis Pola Penyakit Hipertensi Menggunakan Algoritma C4.5,” InfoTekJar (Jurnal Nas. Inform. dan Teknol. Jaringan), vol. 3, no. 2, pp. 116–123, Feb. 2019.
[19] T. Rechkalov and M. Zymbler, “A Study of Euclidean Distance Matrix Computation on Intel Many-Core Processors,” in Communications in Computer and Information Science, vol. 910, no. August, 2018, pp. 200–215.
[20] D. Berrar, “Cross-Validation,” in Encyclopedia of Bioinformatics and Computational Biology, vol. 1–3, no. April, Elsevier, 2019, pp. 542–545.
[21] J. Xu, Y. Zhang, and D. Miao, “Three-way confusion matrix for classification: A measure driven view,” Inf. Sci. (Ny)., vol. 507, no. July, pp. 772–794, Jan. 2020.
[22] B. Afeni, T. Aruleba, and I. Oloyede, “Hypertension Prediction System Using Naive Bayes Classifier,” J. Adv. Math. Comput. Sci., vol. 24, no. 2, pp. 1–11, Jan. 2017.
[23] D. LaFreniere, F. Zulkernine, D. Barber, and K. Martin, “Using machine learning to predict hypertension from a clinical dataset,” in 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016, pp. 1–7.
[24] A. Muzakir and R. A. Wulandari, “Model Data Mining sebagai Prediksi Penyakit Hipertensi Kehamilan dengan Teknik Decision Tree,” Sci. J. Informatics, vol. 3, no. 1, pp. 19–26, Jun. 2016.
[25] E. B. Budianita, “Penerapan Metode Learning Vector Quantization2 (LVQ 2) Untuk Menentukan Gangguan Kehamilan Trimester I,” J. Sains dan Teknol. Ind., vol. 15, no. 2, p. 144, Jun. 2018.