Using Artificial Intelligence Methods for Detection of HCV-Caused Diseases

Öz

The Hepatitis C Virus (HCV) can cause chronic diseases and even lead to more serious conditions such as cirrhosis and fibrosis. Early detection of HCV infection is crucial to prevent these outcomes. However, in the early stages of infection, when symptoms are not yet evident, patients rarely undergo HCV testing. This highlights the need for alternative materials to guide HCV testing for early detection of the disease. In this study, we investigate the use of artificial intelligence technology to determine the disease status of individuals using blood data. A total of 615 individuals were included in the study. Preprocessing, filtering, feature selection, and classification processes were applied to the blood data. The correlation method was used for feature selection, where the features with high correlation values were selected and given as input to five different classification algorithms. The results of the study showed that the K-Nearest Neighbor (KNN) algorithm achieved the best classification success for detecting HCV patients, with a rate of 99.1%. This research demonstrates that artificial intelligence technology can be an effective tool for early detection of HCV-related diseases. The results indicate that the KNN algorithm can provide clear information about hepatitis infection from different blood values. Future studies can explore the use of other AI techniques and expand the sample size to improve the accuracy of the model.

Anahtar Kelimeler

• Hepatitis C virus
• k-nearest neighbors
• preprocessing
• machine learning
• classification

Kaynakça

1. [1] CDC, "Viral Hepatitis", https://www.cdc.gov/hepatitis/hcv/index.htm, (2020).
2. [2] ECDC, "Hepatitis C", https://www.ecdc.europa.eu/en/hepatitis-c, (2022).
3. [3] Durmuş, M. E., "Buz Dağının Görünen Kısmı: Hcv Pozitif Hastalarda Tedaviye Ulaşma Oranları, Hekimlerin Yaklaşım Ve Farkındalıklarının Değerlendirilmesi", T.C. Sağlık Bilimleri Üniversitesi Antalya Sağlık Uygulama ve Araştırma Merkezi, Antalya, (2022).
4. [4] Feinstone, S.M., Kapikian, A.Z., Purcell, R.H., Alter, H.J., Holland, P.V., "Transfusion-Associated Hepatitis Not Due to Viral Hepatitis Type A or B", New England Journal of Medicine, vol. 292, no. 15, pp. 767–770, 1975, doi: 10.1056/NEJM197504102921502.
5. [5] WHO, "Hepatitis C." Jun. 2022.
6. [6] Dumanoğlu, B., "İstanbul medeniyet üniversitesi Göztepe Eğitim ve Araştırma Hastanesi`nde 2016-2018 yılları arasında direkt etkili antiviral tedavi alan kronik hepatit C hastalarının klinik, laboratuvar ve demografik özelliklerinin retrospektif incelenmesi", https://acikbilim.yok.gov.tr/handle/20.500.12812/290084, (2019).
7. [7] Maheshwari, A., Thuluvath, P. J., "Management of acute hepatitis C", Clin Liver Dis, 14(1) (2010) : 169-176.
8. [8] Strader, D. B., Wright, T., Thomas, D. L., Seeff, L. B., "Diagnosis, management, and treatment of hepatitis C", Hepatology 39(4) (2004) : 1147-1171.

9. [9] Demir, N., Kuncan, M., Kaya, Y., Kuncan, F., "Multi-Layer Co-Occurrence Matrices for Person Identification from ECG Signals.", Traitement du Signal 39(2) (2022).
10. [10] Sarmento, R., "Hepatitis C Records - A Complete Statistical Analysis.", Jan. 2021. doi: 10.13140/RG.2.2.22345.16481.
11. [11] Ahammed, K., Satu, M.S., Khan, M.I., Whaiduzzaman, M., "Predicting infectious state of hepatitis C virus affected patient’s applying machine learning methods", in 2020 IEEE Region 10 Symposium (TENSYMP), (2020) : 1371-1374.
12. [12] Syafa’ah, L., Zulfatman, Z., Pakaya, I., Lestandy, M., "Comparison of Machine Learning Classification Methods in Hepatitis C Virus", Jurnal Online Informatika 6(1) (2021) : 73, Jun., doi: 10.15575/join.v6i1.719.
13. [13] Rigg, J., Doyle, O., McDonogh, N., Leavitt, N., Ali, R., Son, A., Kreter, B., "Finding undiagnosed patients with hepatitis C virus: an application of machine learning to US ambulatory electronic medical records", BMJ Health Care Inform. 30(1) (2023) doi: 10.1136/bmjhci-2022-100651.
14. [14] Singh, U., Gourisaria, M.K., Mishra, B.K., "A Dual Dataset approach for the diagnosis of Hepatitis C Virus using Machine Learning", in 2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022, 2022. doi: 10.1109/CONECCT55679.2022.9865758.
15. [15] Lichtinghagen, Ralf., Klawonn, F., Hoffmann, G., "UCI Machine Learning Repository", Available: http://archive.ics.uci.edu/ml/datasets/HCV+data, (2020).
16. [16] Hall, M.A., "Correlation-based feature selection for machine learning", The University of Waikato, (1999).
17. [17] Freeman, J.A., Skapura, D.M., "Neural networks: algorithms, applications, and programming techniques.", Addison Wesley Longman Publishing Co., Inc., (1991).
18. [18] Negnevitsky, M., "Artificial intelligence: a guide to intelligent systems.", Pearson education, (2005).
19. [19] Kim, H., Koehler, G.J., "Theory and practice of decision tree induction", Omega (Westport) 23(6) (1995) : 637-652.
20. [20] Suthaharan, S., Suthaharan, S., "Decision tree learning", Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning (2016) : 237–269.
21. [21] Altman, N.S., "An introduction to kernel and nearest-neighbor nonparametric regression", Am Stat 46(3) (1992) : 175-185,.
22. [22] Cover, H., Hart, P., "Nearest neighbor pattern classification", IEEE Trans. Inf. Theory 13(1) (1953) : 21-27.