Artificial Neural Network Model for Hepatitis C Stage Detection
DOI:
https://doi.org/10.46603/ejcee.v1i1.6Keywords:
Hepatitis C Detection, Artificial Neural Network, Machine Learning, Clinical Decision, Support SystemAbstract
Hepatitis C is a liver disease caused by the hepatitis C virus (HCV). In 2015, WHO reports that 71 million people were living with HCV, and 1.34 million died. In 2017, 13.1 million infected people knew their diagnosis and around 5 million patients were treated. HCV can cause acute and chronic hepatitis, where 20% of chronic hepatitis progresses to final-stage chronic liver cancer. Currently, no vaccine of HCV exists, and no effective treatments are available for demolishing the progression of hepatitis C. So spotting the stages of the disease is essential for diagnostic and therapeutic management of infected patients. This paper attempts to detect stages of hepatitis C virus so that further diagnosis and medication of hepatitis patients can be prescribed. It uses a supervised artificial neural network to make a prediction. Evaluation of results is done by cross-validation using the holdout method. Hepatitis C Egyptian-patients' dataset from UCI Machine Learning Repository is used for feeding the algorithms. The research succeeds to detect the hepatitis C stages and achieves an accuracy of 97%.
Downloads
References
World Health Organization, "Global hepatitis report 2017", https://apps.who.int/iris/bitstream/handle/10665/255016/9789241565455-eng.pdf (accessed Feb. 1, 2020)
Y. Huang, W.B. De Boer, L.A. Adams, G. MacQuillan, M.K. Bulsara, and G.P. Jeffrey, "Image analysis of liver biopsy samples measures fibrosis and predicts clinical outcome." Journal of Hepatology, vol. 61, no. 1, pp. 22-27, Jul. 2014.
S. O. Hussien, S. S. Elkhatem, N. Osman and A. O. Ibrahim, "A review of data mining techniques for diagnosing hepatitis," 2017 Sudan Conference on Computer Science and Information Technology (SCCSIT), Elnihood, 2017, pp. 1-6, doi: 10.1109/SCCSIT.2017.8293064.
C.T. Wai, J.K. Greenson, R.J.Fontana, J.D. Kalbfleisch, J.A. Marrero, H.S. Conjeevaram, and A.S.F Lok, "A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C." Hepatology, vol. 38, no. 2, pp. 518-526, Aug. 2003.
N. Yarraguntla, N. Tirumala, S. Shameem and K. s. rao, "Detection of Hepatitis viruses (HBV, HAV, HCV) in serum using MEMS based Bio-Sensor," 2018 Second International Conference on Computing Methodologies and Communication (ICCMC), Erode, 2018, pp. 405-409, doi: 10.1109/ICCMC.2018.8487679.
UC Irvine Machine Learning Repository "Hepatitis C Virus (HCV) for Egyptian patients Data Set", 2020. [Online]. Available:https://archive.ics.uci.edu/ml/datasets/Hepatitis+C+Virus+%28HCV%29+for+Egyptian+patients
A. Gomaa, N. Allam, A. Elsharkway, M. El Kassas and I. Waked, "Hepatitis C infection in Egypt: prevalence, impact and management strategies." Hepatic Medicine: Evidence and Research, vol. 8, pp. 17, Aug. 2017.
B. S. Alshamrani, and A. H. Osman. "Investigation of hepatitis disease diagnosis using different types of neural network algorithms." International Journal of Computer Science and Network Security (IJCSNS), vol. 17, no. 2, pp. 242, Feb. 2017.
K. B. Nahato, K. H. Nehemiah and A. Kannan. "Hybrid approach using fuzzy sets and extreme learning machine for classifying clinical datasets." Informatics in Medicine Unlocked, Vol. 2, no. 2, pp. 1-11, Jan. 2016.
J. S. Sartakhti, H. Z. Mohammad, and M. Kourosh. "Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)." Computer Methods and Programs in Biomedicine, vol. 108, no. 2, pp. 570-579, Nov. 2012.
D. Kumar, G. Senthil, Sathyadevi, and S. Sivanesh. "Decision support system for medical diagnosis using data mining." International Journal of Computer Science, vol. 8, no. 3, pp. 147, May. 2011.
W. Ahmad, A. Ahmad, A. Iqbal, M. Hamayun, A. Hussain, G. Rehman, S. Khan, U.U. Khan, D. Khan and L. Huang., "Intelligent hepatitis diagnosis using adaptive neuro-fuzzy inference system and information gain method," Soft Computing, vol. 23, no. 21, pp. 10931-10938, Nov 2019.
N. H. Barakat, S. H. Barakat, and N. Ahmed, "Prediction and Staging of Hepatic Fibrosis in Children with Hepatitis C Virus: A Machine Learning Approach," Healthcare Informatics Research, vol. 25, no. 3, pp. 173-181, Jul 2019.
H. Chown, "A comparison of machine learning algorithms for the prediction of Hepatitis C NS3 protease cleavage sites," Eurobiotech Journal, vol. 3, no. 4, pp. 167-174, Oct 2019.
M.A. Konerman, L.A. Beste, T. Van, B. Liu, X. Zhang, J. Zhu, S.D. Saini, G.L. Su, B.K. Nallamothu and G.N. Ioannou., "Machine learning models to predict disease progression among veterans with hepatitis C virus," Plos One, vol. 14, no. 1, p. 14, Jan 2019.
A.J. Mueller-Breckenridge, F. Garcia-Alcalde, S. Wildum, S.L. Smits, A. Robert, M.J. van Campenhout, W.P. Brouwer, J. Niu, J.A. Young and I. Najera, "Machine-learning based patient classification using Hepatitis B virus full-length genome quasispecies from Asian and European cohorts," Scientific Reports, vol. 9, p. 12, Dec 2019.
M. Nilashi, H. Ahmadi, L. Shahmoradi, O. Ibrahim, and E. Akbari, "A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique," Journal of Infection and Public Health, vol. 12, no. 1, pp. 13-20, Jan-Feb 2019.
L. Parisi, N. RaviChandran, and M. L. Manaog, "A novel hybrid algorithm for aiding prediction of prognosis in patients with hepatitis," Neural Computing & Applications, vol. 32, no. 8, pp. 3839-3852, Apr 2020.
X. Tian, Y. Chong, Y. Huang, P. Guo, M. Li, W. Zhang, Z. Du, X. Li, Y. Hao, "Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance," Computational and Mathematical Methods in Medicine, vol. 2019, p. 6915850, 2019.
Y. Wang, Z. C. Du, W. R. Lawrence, Y. Huang, Y. Deng, and Y. T. Hao, "Predicting Hepatitis B Virus Infection Based on Health Examination Data of Community Population," International Journal of Environmental Research and Public Health, vol. 16, no. 23, p. 13, Dec 2019.
D. Sarma, W. Alam, I. Saha, M. N. Alam, M. J. Alam and S. Hossain, "Bank Fraud Detection using Community Detection Algorithm," 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2020, pp. 642-646.
S. Hossain, D. Sarma, T. Mittra, M. N. Alam, I. Saha and F. T. Johora, "Bengali Hand Sign Gestures Recognition using Convolutional Neural Network," 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2020, pp. 636-641.
S. Hossain, A. Abtahee, I. Kashem, M. M. Hoque, and I. H. Sarker, "Crime Prediction Using Spatio-Temporal Data," in Computing Science, Communication and Security, Singapore, 2020, pp. 277-289: Springer Singapore.
H. Alqahtani, I.H. Sarker, A. Kalim, S.M.M. Hossain, S. Ikhlaq and S. Hossain, "Cyber Intrusion Detection Using Machine Learning Classification Techniques," in Computing Science, Communication and Security, Singapore, 2020, pp. 121-131: Springer Singapore.
S. Hossain, F. Islam, R. Karim and K.N. Siddique, "A Critical Comparison between Distributed Database Approach and Data Warehousing Approach." International Journal of Scientific & Engineering Research, Article 5.1 (2014): 196-201.
S. Hossain, D. Sarma, F. Tuj-Johora, J. Bushra, S. Sen and M. Taher, "A Belief Rule Based Expert System to Predict Student Performance under Uncertainty," in 2019 22nd International Conference on Computer and Information Technology (ICCIT), 2019, pp. 1-6.
F. Ahmed, Fatema-Tuj-Johora, R. J. Chakma, S. Hossain and D. Sarma, "A Combined Belief Rule based Expert System to Predict Coronary Artery Disease," in 2020 International Conference on Inventive Computation Technologies (ICICT), 2020, pp. 252-257.
S. Hossain, D. Sarma, R. J. Chakma, W. Alam, M. M. Hoque, and I. H. Sarker, "A Rule-Based Expert System to Assess Coronary Artery Disease Under Uncertainty," in Computing Science, Communication and Security, Singapore, 2020, pp. 143-159: Springer Singapore.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 EDU Journal of Computer and Electrical Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
