Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C

Jimmy Tjen; Valentino Pratama

doi:10.52158/jacost.v4i2.556

Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C

Jimmy Tjen Universitas Widya Dharma Pontianak https://orcid.org/0000-0002-1040-0839
Valentino Pratama SMA Santo Paulus Pontianak

DOI: https://doi.org/10.52158/jacost.v4i2.556
I will put the dimension here

Keywords: distance correlation, Hepatitis C, Diagnostic pathway, Machine learning, Classification tree

Abstract

Hepatitis is considered to be one of the most dangerous diseases, which often leads to death if not handled properly. Thus, early detection via precise diagnosis is needed in order to prevent the unfortunate event. This research aims to provide a novel hepatitis C diagnosis based on the machine learning algorithm, which is the classification tree from the decision tree learning and the distance correlation, which measures the Euclidean distance between 2 vectors. In particular, the goal is to develop a low computational cost yet precise algorithm for diagnosing the possibility of whether a person is being infected with Hepatitis C or not. Based on the experiment, the distance correlation-based classification tree algorithm outperforms the classical classification tree algorithm by around 3% while using only 7 features instead of 12 as in the classical algorithm. Furthermore, the algorithm identified albumin (ALB), Creatinine (CREA), Bilirubin (BIL), Aspartate Transaminase (AST) and Cholesterol (CHOL) as significant risk factors in determining whether someone is potentially infected with hepatitis C or not, with Creatinine is identified as the most important parameter among all 5 parameters mentioned above.

Downloads

Download data is not yet available.

References

D. Bradshaw, J. L. Mbisa, A. M. Geretti, B. J. Healy, G. S. Cooke, G. R. Foster, E. C. Thomson, J. McLauchlan, K. Agarwal and C. a. o. Sabin, "Consensus recommendations for resistance testing in the management of chronic hepatitis C virus infection: Public Health England HCV Resistance Group," Journal of Infection, vol. 76, no. 9, pp. 503-512, 2019.

M. G. Ghany, T. R. Morgan and h. C. g. p. AASLD-IDSA, "Hepatitis C guidance 2019 update: American Association for the Study of Liver Diseases--Infectious Diseases Society of America recommendations for testing, managing, and treating hepatitis C virus infection," Hepatology, vol. 71, no. 2, pp. 686-721, 2020.

S. Blach, N. A. Terrault, F. Tacke, I. Gamkrelidze, A. Craxi, J. Tanaka, I. Waked, G. J. Dore, Z. Abbas and A. R. a. o. Abdallah, "Global change in hepatitis C virus prevalence and cascade of care between 2015 and 2020: a modelling study," The Lancet Gastroenterology & Hepatology, vol. 7, no. 5, pp. 396-415, 2022.

A. Souri, M. Y. Ghafour, A. M. Ahmed, F. Safara, A. Yamini and M. Hoseyninezhad, "A new machine learning-based healthcare monitoring model for student’s condition diagnosis in Internet of Things environment," Soft Computing, vol. 24, no. 22, pp. 17111-17121, 2020.

A. B. Shatte, D. M. Hutchinson and S. J. Teague, "Machine learning in mental health: a scoping review of methods and applications," Psychological medicine, vol. 49, no. 9, pp. 1426-1448, 2019.

Y. Lei, B. Yang, X. Jiang, F. Jia, N. Li and A. K. Nandi, "Applications of machine learning to machine fault diagnosis: A review and roadmap," Mechanical Systems and Signal Processing, vol. 138, p. 106587, 2020.

L. Breiman, J. H. Friedman, R. A. Olshen and C. J. Stone, Classification and regression trees, Routledge, 2017.

G. Hoffmann, A. Bietenbeck, R. Lichtinghagen and F. Klawonn, "Using machine learning techniques to generate laboratory diagnostic pathways—a case study," J Lab Precis Med, vol. 3, no. 6, 2018.

F. Smarra, J. Tjen and A. D'Innocenzo, "Learning methods for structural damage detection via entropy-based sensors selection," International Journal of Robust and Nonlinear Control, vol. 32, no. 10, pp. 6035-6067, 2022.

J. Tjen, G. Hoendarto and T. Darmanto, "Ensemble of the Distance Correlation-Based and Entropy-Based Sensor Selection for Damage Detection," in 2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), Online Virtual meeting, 2022.

J. Tjen, G. Hoendarto, T. Darmanto and T. Willay, "Distance Correlation-Based Regression Tree Algorithm For Structural Damage Detection," JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 10, no. 2, pp. 440-455, 2023.

J. Tjen, F. Smarra and A. D’Innocenzo, "An entropy-based sensor selection algorithm for structural damage detection," in 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Online virtual meeting, 2020.

J. Tjen, "Algoritma Pendeteksi Kerusakan Struktur Bangunan Berbasis Korelasi Jarak dan Metode Kuadrat Terkecil Parsial," JEPIN (Jurnal Edukasi dan Penelitian Informatika), vol. 8, no. 3, pp. 459-469, 2022.

R. Huang, C. Cui, W. Sun and D. Towey, "Is euclidean distance the best distance measurement for adaptive random testing?," in IEEE 13th International Conference on Software Testing, Validation and Verification (ICST), Porto, 2020.

University of California Irvine, "HCV data," 2020. [Online]. Available: https://archive.ics.uci.edu/dataset/571/hcv+data. [Accessed 13 June 2023].

G. James, D. Witten, T. Hastie, R. Tibshirani and J. Taylor, "Tree-based methods," in An Introduction to Statistical Learning: with Applications in Python, Springer, 2023, pp. 331-366.

B. Cerbu, M. L. Grigoras, F. Bratosin, I. Bogdan, C. Citu, A. V. Bota, M. Timircan, M. L. Bratu, M. C. Levai and I. Marincu, "Laboratory Profile of COVID-19 Patients with Hepatitis C-Related Liver Cirrhosis," Journal of Clinical Medicine, vol. 11, no. 3, p. 652, 2020.

M. F. Alsaffar, "Elevation of some biochemical and immunological parameters in hemodialysis patients suffering from hepatitis C virus infection in Babylon Province," Indian Journal of Forensic Medicine & Toxicology, vol. 15, no. 3, pp. 2354-2362, 2021.

S. Y. Han, H. Y. Woo, J. Heo, S. G. Park, S. I. Pyeon, Y. J. Park, D. U. Kim, G. H. Kim, H. H. Kim and G. Am Song, "The predictors of sustained virological response with sofosbuvir and ribavirin in patients with chronic hepatitis C genotype 2," The Korean Journal of Internal Medicine, vol. 36, no. 3, p. 544, 2021.

S. Pol, L. Parlati and M. Jadoul, "Hepatitis C virus and the kidney," Nature Reviews Nephrology, vol. 15, no. 2, pp. 73-86, 2019.

Published

2023-11-18

How to Cite

[1]

J. Tjen and V. Pratama, “Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C”, J. Appl. Comput. Sci. Technol., vol. 4, no. 2, pp. 124 - 130, Nov. 2023.

Download Citation

Issue

Vol 4 No 2 (2023): Desember 2023

Section

Articles

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Pernyataan Hak Cipta dan Lisensi

Dengan mengirimkan manuskrip ke Journal of Applied Computer Science and Technology (JACOST), penulis setuju dengan kebijakan ini. Tidak diperlukan persetujuan dokumen khusus.

Hak cipta pada setiap artikel adalah milik penulis.
Penulis mempertahankan semua hak mereka atas karya yang diterbitkan, tak terbatas pada hak-hak yang diatur dalam laman ini.
Penulis mengakui bahwa Journal of Applied Computer Science and Technology (JACOST) sebagai yang pertama kali mempublikasikan dengan lisensi Creative Commons Atribusi 4.0 Internasional (CC BY-SA).
Penulis dapat memasukan tulisan secara terpisah, mengatur distribusi non-ekskulif dari naskah yang telah terbit di jurnal ini kedalam versi yang lain (misal: dikirim ke respository institusi penulis, publikasi kedalam buku, dll), dengan mengakui bahwa naskah telah terbit pertama kali pada Journal of Applied Computer Science and Technology (JACOST);
Penulis menjamin bahwa artikel asli, ditulis oleh penulis yang disebutkan, belum pernah dipublikasikan sebelumnya, tidak mengandung pernyataan yang melanggar hukum, tidak melanggar hak orang lain, tunduk pada hak cipta yang secara eksklusif dipegang oleh penulis.
Jika artikel dipersiapkan bersama oleh lebih dari satu penulis, setiap penulis yang mengirimkan naskah menjamin bahwa dia telah diberi wewenang oleh semua penulis bersama untuk menyetujui hak cipta dan pemberitahuan lisensi (perjanjian) atas nama mereka, dan setuju untuk memberi tahu rekan penulis persyaratan kebijakan ini. Journal of Applied Computer Science and Technology (JACOST) tidak akan dimintai pertanggungjawaban atas apa pun yang mungkin timbul karena perselisihan internal penulis.

Lisensi :

Journal of Applied Computer Science and Technology (JACOST) diterbitkan berdasarkan ketentuan Lisensi Creative Commons Atribusi 4.0 Internasional (CC BY-SA). Lisensi ini mengizinkan setiap orang untuk :.

Berbagi — menyalin dan menyebarluaskan kembali materi ini dalam bentuk atau format apapun;
Adaptasi — menggubah, mengubah, dan membuat turunan dari materi ini untuk kepentingan apapun.

Lisensi :

Atribusi — Anda harus mencantumkan nama yang sesuai, mencantumkan tautan terhadap lisensi, dan menyatakan bahwa telah ada perubahan yang dilakukan. Anda dapat melakukan hal ini dengan cara yang sesuai, namun tidak mengisyaratkan bahwa pemberi lisensi mendukung Anda atau penggunaan Anda.
BerbagiSerupa — Apabila Anda menggubah, mengubah, atau membuat turunan dari materi ini, Anda harus menyebarluaskan kontribusi Anda di bawah lisensi yang sama dengan materi asli.