APPLICATION OF DEEP BOLTZMANN MACHINE IN DIAGNOSIS PROCESSES OF HEPATITIS TYPES B & C

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Volume 5 (1), June 2022, Pages 112-130

Hadis Oftadeh¹ and Mohammad Manthouri²

¹ Islamic Azad University, Tehran, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it.

² Shahed University, Tehran, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Correct diagnosis of diseases is the main problem in medicine. Artificial intelligence and learning methods have been developed to solve problems in many fields, such as biology and medical sciences. Correct diagnosis before treatment is the most challenging and the first step in achieving proper cures. The primary objective of this paper is to introduce an intelligent system that develops beyond the deep neural network. It can diagnose and distinguish between Hepatitis types B and C by using a set of general tests for liver health. The deep network used in this research is the Deep Boltzmann Machine (DBM). Learning components within Restricted Boltzmann Machine (RBM) lead to intended results. The RBMs extract features to be used in an efficient classification process. An RBM is robust computing and well-suited to extract high-level features and diagnose hepatitis B and C. The method was tested on general items in laboratory tests that check the liver’s health. The DBM could predict hepatitis type B and C with an accuracy between 90.1% and 92.04%. Predictive accuracy was obtained with10-fold cross-validation. Compared with other methods, simulation results on DBM architecture reveal the proposed method’s efficiency in diagnosing Hepatitis B and C. What made this approach successful was a deep learning network in addition to discovering communication and extracting knowledge from the data.

Keywords:

Deep learning, Restricted Boltzmann Machine, Hepatitis, Neural Network, Classification.

DOI: https://doi.org/10.32010/26166127.2022.5.1.112.130

Reference 

AbuSharekh, E. K., & Abu-Naser, S. S. (2018). Diagnosis of hepatitis virus using artificial neural network. International Journal of Academic Pedogogical Research, 2(11), 1-7.

Ahn, J. C., Connell, A., Simonetto, D. A., Hughes, C., & Shah, V. H. (2021). Application of artificial intelligence for the diagnosis and treatment of liver diseases. Hepatology, 73(6), 2546-2563.

Akbar, W., Wu, W. P., et al. (2020). Development of hepatitis disease detection system by exploiting sparsity in linear support vector machine to improve strength of adaboost ensemble model. Mobile Information Systems, 2020.

Baumert, T. F., Berg, T., Lim, J. K., & Nelson, D. R. (2019). Status of direct-acting antiviral therapy for hepatitis C virus infection and remaining challenges. Gastroenterology, 156(2), 431-445.

Bereciartua, A., Picon, A., Galdran, A., & Iriondo, P. (2016). 3D active surfaces for liver segmentation in multisequence MRI images. Computer Methods and Programs in Biomedicine, 132, 149-160.

Bhardwaj, A., & Tiwari, A. (2015). Breast cancer diagnosis using genetically optimized neural network model. Expert Systems with Applications, 42(10), 4611-4620. 

Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT’2010 (pp. 177-186). Physica-Verlag HD.

Bu, Y., Zhao, G., Luo, A. L., Pan, J., & Chen, Y. (2015). Restricted Boltzmann machine: a non-linear substitute for PCA in spectral processing. Astronomy & Astrophysics, 576, A96.

Chen, Y., Luo, Y., et al. (2017). Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B. Computers in biology and medicine, 89, 18-23.

Dataset (2022). https://relational.fit.cvut.cz/dataset/Hepatitis. 

Durot, I., Akhbardeh, A., Sagreiya, H., Loening, A. M., & Rubin, D. L. (2020). A new multimodel machine learning framework to improve hepatic fibrosis grading using ultrasound elastography systems from different vendors. Ultrasound in medicine & biology, 46(1), 26-33.

ECML/PKDD (2002). https://www.cs.helsinki.fi/events/ecmlpkdd/. 

Ferreira, C. A., Gama, J., & Costa, V. S. (2011, October). Constrained sequential pattern knowledge in multi-relational learning. In Portuguese Conference on Artificial Intelligence (pp. 282-296). Springer, Berlin, Heidelberg.

Gadekallu, T. R., Khare, N., et al. (2020). Deep neural networks to predict diabetic retinopathy. Journal of Ambient Intelligence and Humanized Computing, 1-14.

Hashem, S., Esmat, G., et al. (2017). Comparison of machine learning approaches for prediction of advanced liver fibrosis in chronic hepatitis C patients. IEEE/ACM transactions on computational biology and bioinformatics, 15(3), 861-868.

Larranaga, P., Calvo, B., et al. (2006). Machine learning in bioinformatics. Briefings in bioinformatics, 7(1), 86-112.

Li, F., Gao, X., & Wan, K. (2018). Training restricted boltzmann machine using gradient fixing based algorithm. Chinese Journal of Electronics, 27(4), 694-703.

Liao, M., Zhao, Y. Q., et al. (2017). Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching. Computer methods and programs in biomedicine, 143, 1-12.

Mahmoud, A. M., Alrowais, F., & Karamti, H. (2020). A hybrid deep contractive autoencoder and restricted boltzmann machine approach to differentiate representation of female brain disorder. Procedia Computer Science, 176, 1033-1042. 

Min, S., Lee, B., & Yoon, S. (2017). Deep learning in bioinformatics. Briefings in bioinformatics, 18(5), 851-869.

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics, 19(6), 1236-1246.

Movahedi, M. M., Zamani, A., et al. (2020). Automated analysis of ultrasound videos for detection of breast lesions. Middle East Journal of Cancer, 11(1), 80-90.

Nilashi, M., Ahmadi, H., Shahmoradi, L., Ibrahim, O., & Akbari, E. (2019). A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. Journal of infection and public health, 12(1), 13-20.

Nunavath, V., Goodwin, M., Fidje, J. T., & Moe, C. E. (2018, September). Deep neural networks for prediction of exacerbations of patients with chronic obstructive pulmonary disease. In International Conference on Engineering Applications of Neural Networks (pp. 217-228). Springer, Cham.

Podlaha, O., Revill, P., et al. (2017). Whole-genome deep sequencing of hepatitis B virus in chronic hepatitis B patients reveals single nucleotide variants associated with baseline HBV DNA levels and HBeAg status. Journal of Hepatology, 1(66), S679.

Quer, J., Rodríguez-Frias, F., et al. (2017). Deep sequencing in the management of hepatitis virus infections. Virus research, 239, 115-125.

Razavi, H. (2020). Global epidemiology of viral hepatitis. Gastroenterology Clinics, 49(2), 179-189. 

Remita, M. A., Halioui, A., et al. (2017). A machine learning approach for viral genome classification. BMC bioinformatics, 18(1), 1-11.

Schulte, O., Bina, B., et al. (2013, April). A hierarchy of independence assumptions for multi-relational Bayes net classifiers. In 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) (pp. 150-159). IEEE.

Sia, F., Alfred, R., & Chin, K. O. (2013, August). Learning relational data based on multiple instances of summarized data using DARA. In International Multi-Conference on Artificial Intelligence Technology (pp. 293-301). Springer, Berlin, Heidelberg. 

Sun, T., Zhou, B., Lai, L., & Pei, J. (2017). Sequence-based prediction of protein protein interaction using a deep-learning algorithm. BMC bioinformatics, 18(1), 1-8. 

Taherkhani, A., Cosma, G., & McGinnity, T. M. (2018). Deep-FS: A feature selection algorithm for Deep Boltzmann Machines. Neurocomputing, 322, 22-37.

Upadhya, V., & Sastry, P. S. (2019). An overview of restricted Boltzmann machines. Journal of the Indian Institute of Science, 99(2), 225-236. 

Varsamou, M., & Antonakopoulos, T. (2019, September). Classification using Discriminative Restricted Boltzmann Machines on Spark. In 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (pp. 1-6). IEEE.

Wang, C., Tan, X. P., Tor, S. B., & Lim, C. S. (2020). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, 36, 101538.

Wani, M. A., Bhat, F. A., Afzal, S., & Khan, A. I. (2020). Advances in deep learning. Springer.

World Health Organization. (2017). Regional action plan for the implementation of the global health sector strategy on viral hepatitis 2017–2021 (No. WHO-EM/STD/188/E). World Health Organization. Regional Office for the Eastern Mediterranean.

Xiao, Y., Wu, J., Lin, Z., & Zhao, X. (2018). A deep learning-based multi-model ensemble method for cancer prediction. Computer methods and programs in biomedicine, 153, 1-9.

Xiao, Y., Xing, C., Zhang, T., & Zhao, Z. (2019). An intrusion detection model based on feature reduction and convolutional neural networks. IEEE Access, 7, 42210-42219.

Zhang, Y., Li, P., & Wang, X. (2019). Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access, 7, 31711-31722.