Diagnosis And Detection Of Hepatitis Disease Using ML

Swetha Talabathula, Gangotri Kongarani, Kala Devi Nadimpalli, Bhairava Varma Pesingi, Jaswanthi Korasikha, V. Sai Priya


The main goal of this research is to determine the optimum method for diagnosing and identifying hepatitis while also taking future patient expectations into consideration. This study involved the completion of a comparative report between several machine learning technologies and neural networks. The precision rate and mean square error both affect the exhibition metric. The Machine Learning (ML) techniques, such as Support Vector Machines (SVM), K Nearest Neighbor (KNN), and Artificial Neural Network (ANN), were thought of as the characterising and anticipating tools for diagnosing hepatitis infection. In light of the accuracy of the sickness diagnosis prediction, a brief report on the aforementioned algorithms was completed.


Ghumbre S. U.; Ghalot A.A, “Hepatitis B Diagnosis using Logical Inference And Self Organizing Map”, 2008 ; Journal of Computer Science ISSN 1549-3636.

M. A. Chinnaratha, G. P. Jeffrey, G. Macquillan, E. Rossi, B. W. D. Boer, D. J. Speers, and L. A. Adams, “Prediction of morbidity and mortality in patients with chronic hepatitis c by non-invasive liver fibrosis models,” Liver International, vol. 34, no. 5, pp. 720–727, 2014.

Roslina, A. H., and A. Noraziah. "Prediction of hepatitis prognosis using Support Vector Machines and Wrapper Method." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, vol. 5, pp. 2209-2211. IEEE, 2010.

G. H. Haydon, R. Jalan, M. Alakorpela, Y. Hiltunen, J. Hanley, L. M. Jarvis, C. A. Ludlum, and P. C. Hayes, “Prediction of cirrhosis in patients with chronic hepatitis c infection by artificial neural network analysis of virus and clinical factors,” Journal of Viral Hepatitis, vol. 5, no. 4, pp. 255–264, 2010.

Atif Khan, John A. Doucette, Robin Cohen, “Integrating Machine Learning into a Medical Decision Support System to Address the Problem of Missing Patient Data”, 2012 IEEE DOI 10.1109/ICMLA.2012.82.

Uhmn, Saangyong, Dong-Hoi Kim, Young-Woong Ko, Sungwon Cho, Jaeyoun Cheong, and Jin Kim. "A study on application of single nucleotide polymorphism and machine learning techniques to diagnosis of chronic hepatitis." Expert Systems 26, no. 1 (2009): 60- 69.

KayvanJoo, Amir Hossein, Mansour Ebrahimi, and Gholamreza Haqshenas. "Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms." BMC research notes 7, no. 1 (2014): 565.

Vijayarani, S., and S. Dhayanand. "Liver disease prediction using SVM and Naïve Bayes algorithms." International Journal of Science, Engineering and Technology Research (IJSETR) 4, no. 4 (2015): 816- 820.

Sartakhti, Javad Salimi, Mohammad Hossein Zangooei, and Kourosh Mozafari. "Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVMSA)." Computer methods and programs in biomedicine 108, no. 2 (2012): 570-579.

Uttreshwar, Ghumbre Shashikant, and A. A. Ghatol. "Hepatitis B diagnosis using logical inference and generalized regression neural networks." In 2009 IEEE International Advance Computing Conference, pp. 1587-1595. IEEE, 2009.

Ba-Alwi, Fadl Mutaher, and Houzifa M. Hintaya. "Comparative study for analysis the prognostic in hepatitis data: data mining approach." spinal cord 11 (2013): 12.

Yildirim, Pinar. "Filter based feature selection methods for prediction of risks in hepatitis disease." International Journal of Machine Learning and Computing 5, no. 4 (2015): 258.

Chen, Hui-Ling, Da-You Liu, Bo Yang, Jie Liu, and Gang Wang. "A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis." Expert Systems with Applications 38, no. 9 (2011): 11796-11803.

Lalithamani N., Amrutha C., "Efficient storage and retrieval of medical records using fusion-based multimodal biometrics", International Journal of Computer Aided Engineering and Technology, Volume 10, Issue 5, 2018, Pages 576-588.

Full Text: PDF [Full Text]


  • There are currently no refbacks.

Copyright © 2013, All rights reserved.| ijseat.com

Creative Commons License
International Journal of Science Engineering and Advance Technology is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJSEat , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.