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Wavelet-based Multifractal Spectrum Estimation in Hepatitis Virus Classification Models by Using Artificial Neural Network Approach

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Global Virology III: Virology in the 21st Century

Abstract

 Fractal and multifractal geometries have been applied extensively in various medical signals which exhibit fractal characteristics. Application of such geometries rests on the estimation of fractal features. Within this framework, various methods have been proposed for the estimation of the multifractal spectral or fractal dimension of a particular signal. Wavelet transform modulus maxima (WTMM) is one of the methods employed for the detection of fractal dimension of a signal. It was developed for the characterization of signal singularities. Hepatitis, inflammation of the liver, may prove to be a serious disease with serious potential risks. This study proposes an alternative method for the classification of hepatitis virus as per die/live with the use of two aspects, namely multifractal analysis and Artificial Neural Network (ANN). As the first aspect, for the multifractal analysis, Wavelet Transform Modulus Maxima (WTMM) (Multifractal Spectrum estimation) was used with the following stages: (a) WTMM was applied to the hepatitis dataset (self-similar and significant attributes were identified) and wtmm_hepatitis dataset was generated. (b) Continuous Wavelet Transform was applied on the hepatitis dataset (hepatitis_dataset) and wtmm_hepatitis dataset. The second aspect is related to the application of Feed Forward Back Propagation (FFBP) algorithm which is an ANN application with the following steps: (i) FFBP algorithm was applied to both hepatitis dataset (hepatitis_dataset) and (wtmm_hepatitis dataset) to identify the classification as per die/live (ii) The attributes proven to be the most effective were determined based on the results (sensitivity, specificity and accuracy rate). The highest level of accuracy has been obtained from the wtmm_hepatitis dataset. The main contribution of this study is that it has proven to provide an alternative in multifractal spectrum estimation by determining the self-similar and significant attributes through WTMM for the first time in the literature. The proposed method in the study aims at bringing a new frontier in the related fields by placing emphasis on the significance of significant attributes’ characterization to obtain optimal accuracy rates for the solution of problems.

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References

  1. Venkatakrishnan P, Sangeetha S. Singularity detection in human EEG signal using wavelet leaders. Biomed Signal Process Control. 2014;13:282–94. https://doi.org/10.1016/j.bspc.2014.06.002.

    Article  Google Scholar 

  2. Puckovs A, Matvejevs A. Wavelet transform modulus maxima approach for world stock index multifractal analysis. University. Inf Technol Manage Sci. 2013;15(1):76–86. https://doi.org/10.2478/v10313-012-0016-5.

    Article  Google Scholar 

  3. Yasin H, Jilani TA, Danish M. Hepatitis-C classification using data mining techniques. Int J Comput Appl. 2011;24(3):1–6. ISSN 0975-8887.

    Google Scholar 

  4. Nilashi M, Ahmadi H, Shahmoradi L, Ibrahim O, Akbari E. A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. J Infect Public Health. 2019;12(1):13–20. https://doi.org/10.1016/j.jiph.2018.09.009.

    Article  PubMed  Google Scholar 

  5. Almuneef MA, Memish ZA, Balkhy HH, Qahtani M, Alotaibi B, Hajeer A, et al. Epidemiologic shift in the prevalence of Hepatitis A virus in Saudi Arabia: a case for routine Hepatitis A vaccination. Vaccine. 2006;24(27):5599–603. https://doi.org/10.1016/j.vaccine.2006.04.038.

    Article  CAS  PubMed  Google Scholar 

  6. Al-Thaqafy MS, Balkhy HH, Memish Z, Makhdom YM, Ibrahim A, Al-Amri A, et al. Hepatitis B virus among Saudi National guard personnel: seroprevalence and risk of exposure. J Infect Public Health. 2013;6(4):237–45. https://doi.org/10.1016/j.jiph.2012.12.006.

    Article  PubMed  Google Scholar 

  7. Al-Thaqafy MS, Balkhy HH, Memish Z, Makhdom YM, Ibrahim A, Al-Amri A, Al-Thaqafi A. Improvement of the low knowledge, attitude and practice of hepatitis B virus infection among Saudi National Guard personnel after educational intervention. BMC Res Notes. 2012;5(1):597. https://doi.org/10.1186/1756-0500-5-597.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Shepard CW, Finelli L, Alter MJ. Global epidemiology of hepatitis C virus infection. Lancet Infect Dis. 2005;5(9):558–67. https://doi.org/10.1016/S1473-3099(05)70216-4.

    Article  PubMed  Google Scholar 

  9. Wu JC, Chen TZ, Huang YS, Yen FS, Ting LT, Sheng WY, et al. Natural history of hepatitis D viral superinfection: significance of viremia detected by polymerasechain reaction. Gastroenterology. 1995;108(3):796–802.

    Article  CAS  Google Scholar 

  10. Haagsma EB, van den Berg AP, Porte RJ, Benne CA, Vennema H, Reimerink JH, et al. Chronic hepatitis E virus infection in liver transplant recipients. Liver Transp. 2008;14(4):547–53. https://doi.org/10.1002/lt.21480.

    Article  Google Scholar 

  11. Mistler LA, Brunette MF, Marsh BJ, Vidaver RM, Luckoor R, Rosenberg SD. Hepatitis C treatment for people with severe mental illness. Psychosomatics. 2006;47(2):93–107. https://doi.org/10.1176/appi.psy.47.2.93.

    Article  CAS  PubMed  Google Scholar 

  12. Metwally NF, AbuSharekh EK, Abu-Naser SS. Diagnosis of hepatitis virus using artificial neural network. Int J Acad Pedagogical Res. 2018;2(11):1–7. ISSN: 2000-004X.

    Google Scholar 

  13. Jilani TA, Yasin H, Yasin MM. PCA-ANN for classification of Hepatitis-C patients. Int J Comput Appl. 2011;14(7):1–6. ISSN: 0975-8887.

    Google Scholar 

  14. Priya S, Manavalan R. Optimum parameters selection using ACO R algorithm to improve the classification performance of weighted extreme learning machine for hepatitis disease dataset. In: 2018 International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE; 2018. p. 986–91. https://doi.org/10.1109/ICIRCA.2018.8597232.

  15. Karaca Y, Cattani C. Clustering multiple sclerosis subgroups with multifractal methods and self-organizing map algorithm. Fractals. 2017;25(4):1740001. https://doi.org/10.1142/S0218348X17400011.

    Article  Google Scholar 

  16. Karaca Y, Cattani C, Karabudak R. ANN classification of MS subgroups with diffusion limited aggregation. In: Gervasi O, et al., editors. International Conference on Computational Science and Its Applications. ICCSA 2018. Lecture notes in computer science, vol. 10961. Cham: Springer; 2018. p. 121–36. https://doi.org/10.1007/978-3-319-95165-2_9.

    Chapter  Google Scholar 

  17. Arulmurugan R, Anandakumar H. Early detection of lung cancer using wavelet feature descriptor and feed forward back propagation neural networks classifier. In: Hemanth D, Smys S, editors. Lecture notes in computational vision and biomechanics, vol. 28. Cham: Springer; 2018. p. 103–10. https://doi.org/10.1007/978-3-319-71767-8_9.

    Chapter  Google Scholar 

  18. Karaca Y, Aslan Z, Siddiqi AH. 1D Wavelet and partial correlation application for MS subgroup diagnostic classification. Classification. In: Manchanda P, Lozi R, Siddiqi A, editors. Industrial mathematics and complex systems. Industrial and applied mathematics. Singapore: Springer; 2017. p. 171–86. doi.org/10.1007/978-981-10-3758-0_11.

    Chapter  Google Scholar 

  19. Parey A, Singh A. Gearbox fault diagnosis using acoustic signals, continuous wavelet transform and adaptive neuro-fuzzy inference system. Appl Acoust. 2019;147:133–40. https://doi.org/10.1016/j.apacoust.2018.10.013.

    Article  Google Scholar 

  20. Karaca Y, Aslan Z, Cattani C, Galletta D, Zhang Y. Rank determination of mental functions by 1D wavelets and partial correlation. J Med Syst. 2017;41(1):1–10. https://doi.org/10.1007/s10916-016-0606-2.

    Article  Google Scholar 

  21. Karaca Y, Sertbaş A, Bayrak Ş. Classification of erythematous – squamous skin diseases through SVM kernels and identification of features with 1-D continuous wavelet coefficient. In: Gervasi O, et al., editors. International Conference on Computational Science and Its Applications. ICCSA 2018. Lecture notes in computer science, vol. 10961. Cham.: Springer; 2018. p. 107–20. https://doi.org/10.1007/978-3-319-95165-2_8.

    Chapter  Google Scholar 

  22. Cattani C. Fractals and hidden symmetries in DNA. Math Probl Eng. 2010;2010:31. 507056. https://doi.org/10.1155/2010/507056.

    Article  CAS  Google Scholar 

  23. Venkatakrishnan P, Sangeetha S, Sundar M. Measurement of Lipschitz exponent (LE) using wavelet transform modulus maxima (WTMM). Int J Sci Eng Res. 2012;3:6. ISSN 2229-5518.

    Google Scholar 

  24. Izadi H, Innanen K, Lamoureux MP. Continuous wavelet transforms and Lipschitz exponents as a means for analysing seismic data. CREWES Res Rep. 2011;23:1–8.

    Google Scholar 

  25. Legarreta IR, Addison PS, Grubb N, Clegg GR, Robertson CE, Fox KAA, Watson JN. R-wave detection using continuous wavelet modulus maxima. Comput Cardiol. 2003;1(30):565–8. https://doi.org/10.1109/CIC.2003.1291218.

    Article  Google Scholar 

  26. Blake CL, Merz CJ. UCI repository of machine learning databases. 1996. Available from: https://archive.ics.uci.edu/ml/index.php. Accessed 2 Jan 2019.

  27. Mallat S. A wavelet tour of data processing. USA: Elsevier, Academic Press; 1999.

    Google Scholar 

  28. Peng ZK, Chu FL, Peter WT. Singularity analysis of the vibration signals by means of wavelet modulus maximal method. Mech Syst Signal Process. 2007;21(2):780–94. https://doi.org/10.1016/j.ymssp.2005.12.005.

    Article  Google Scholar 

  29. Mallat S, Hwang WL. Singularity detection and processing with wavelets. IEEE Trans Inf Theory. 1992;38(2):617–43. https://doi.org/10.1109/18.119727.

    Article  Google Scholar 

  30. Vrscay ER. A generalized class of fractal-wavelet transforms for image representation and compression. Can J Electr Comput Eng. 1998;23(1–2):69–83. https://doi.org/10.1109/CJECE.1998.7102047.

    Article  Google Scholar 

  31. Tu GJ, Karstoft H. Logarithmic dyadic wavelet transform with its applications in edge detection and reconstruction. Appl Soft Comput. 2015;26:193–201. https://doi.org/10.1016/j.asoc.2014.09.044.

    Article  Google Scholar 

  32. Yuan YT, Li BF, Ma H, Lin J. Ring-projection-wavelet-fractal signatures: a novel approach to feature extraction. IEEE Trans Circuits Syst II: Analog Digital Signal Process. 1998;45(8):1130–4. https://doi.org/10.1109/82.718824.

    Article  Google Scholar 

  33. Jaffard S, Lashermes B, Abry P. Wavelet leaders in multifractal analysis. In: Qian T, Vai MI, Xu Y, editors. Wavelet analysis and applications. Applied and numerical harmonic analysis. Birkhäuser Basel; 2006. p. 201–46. https://doi.org/10.1007/978-3-7643-7778-6_17.

  34. Bujanovic T, Abdel-Qader I. On wavelet transform general Modulus maxima metric for singularity classification in mammograms. Open J Med Imaging. 2013;3(1):17. https://doi.org/10.4236/ojmi.2013.31004.

    Article  Google Scholar 

  35. Karaca Y, Cattani C. Computational methods for data analysis. De Gruyter; 2018. ISBN: 978-3-11-049636-9.

    Google Scholar 

  36. Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw. 1994;5(6):989–93. https://doi.org/10.1109/72.329697.

    Article  CAS  PubMed  Google Scholar 

  37. Saeedi E, Hossain MS, Kong Y. Feed-forward back-propagation neural networks in side-channel information characterisation. J Circuits Syst Comput. 2019;28(1):1950003. https://doi.org/10.1142/S0218126619500038.

    Article  Google Scholar 

  38. The MathWorks. MATLAB (R2018b). Natick: The MathWorks, Inc.; 2018.

    Google Scholar 

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Acknowledgement

The author is genuinely grateful to Professor Carlo Cattani for his academic support and guidance.

Author information

Authors and Affiliations

  1. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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  1. Yeliz Karaca
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Correspondence to Yeliz Karaca .

Editor information

Editors and Affiliations

  1. Department of Internal Medicine, University of South Florida, Tampa, FL, USA

    Paul Shapshak

  2. Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India

    Seetharaman Balaji

  3. Biomedical Informatics 17, Irulan Sandy Annex, Pondicherry, Pondicherry, India

    Pandjassarame Kangueane

  4. Oral Biology and Medicine, CHS 63-090, UCLA School of Dentistry Oral Biology and Medicine, CHS 63-090, Los Angeles, CA, USA

    Francesco Chiappelli

  5. Department of Internal Medicine, University of South Florida, Tampa, FL, USA

    Charurut Somboonwit

  6. Department of Internal Medicine, University of South Florida, Tampa, FL, USA

    Lynette J. Menezes

  7. Department of Internal Medicine, University of South Florida, Tampa, FL, USA

    John T. Sinnott

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Karaca, Y. (2019). Wavelet-based Multifractal Spectrum Estimation in Hepatitis Virus Classification Models by Using Artificial Neural Network Approach. In: Shapshak, P., et al. Global Virology III: Virology in the 21st Century. Springer, Cham. https://doi.org/10.1007/978-3-030-29022-1_4

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