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Analyzing genetic diseases using multimedia processing techniques associative decision tree-based learning and Hopfield dynamic neural networks from medical images

  • Mohammed Al-MaitahEmail author
Intelligent Biomedical Data Analysis and Processing
  • 33 Downloads

Abstract

Genetic diseases are the most common next-generation diseases because of the improper mutation of the genes and DNA. These genetic diseases are failed to predict with an accurate manner in the beginning stage by using the particular genes and related information. So, the genetic diseases are identified in the medical systems by utilizing the hybridization of multimedia techniques such as big data and related soft computing techniques.Initially, the genetic disease-related medical images are collected from healthcare sectors, and from the genetic image, various genetic data are collected from the large amount of datasets in which the major challenge is too high dimensionality that increases the complexity of the genetic disease prediction system. So, in this paper the complexity of the system is reduced by using the associative decision tree-based learning and Hopfield dynamic neural networks (HDNN). After collecting the data from the various resources, the immune clonal selection algorithm approach is used to remove inconsistent data and minimize the dimensionality of data. The selected features are trained by the proposed associative decision tree approach which helps to compare with the testing features using the HDNN that successfully recognize the genetic disease-based features effectively. The excellence of the system is measured with the aid of the experimental outcomes that are corresponding to the forecasting methods such as greedy algorithm, rough set method and artificial bee colony, and the comparison is made with the avail of the accuracy, sensitivity and specificity.

Keywords

Medical image Multimedia tool Genetic diseases Artificial bee colony Associative decision tree-based learning Greedy forward selection Scatter search Hopfield dynamic neural networks 

Notes

Acknowledgements

This project was supported by King Saud University, Deanship of Scientific Research, Community College Research Unit.

Compliance with ethical standards

Conflict of interest

The author declares that he has any conflict of interest.

References

  1. 1.
    Aguas R, Ferguson NM (2013) Feature selection methods for identifying genetic determinants of host species in RNA viruses. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003254
  2. 2.
    Al-Sheshtawi KA, Abdul-Kader HM, Ismail NA (2010) Artificial immune clonal selection classification algorithms for classifying malware and benign processes using API call sequences. IJCSNS 10(4):31Google Scholar
  3. 3.
    Anitha DPV (2013) Feature selection by rough–quick reduct algorithm. Int J Innov Res Sci Eng Technol 2(8):2319Google Scholar
  4. 4.
    Arunkumar N, Mohammed MA, Mostafa SA, Ibrahim DA, Rodrigues JJPC, de Albuquerque VHC (2018) Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks. Concurr Comput Pract Exp.  https://doi.org/10.1002/cpe.4962 Google Scholar
  5. 5.
    Barati M, Ebrahimi M (2016) Identification of genes involved in the early stages of alzheimer disease using a neural network algorithm. Gene Cell Tissue 3(3):e38415.  https://doi.org/10.17795/gct-38415 CrossRefGoogle Scholar
  6. 6.
    Chen H, Cao L, Li Z, Hemanth DJ, Wu L, de Albuquerque VHC, Shi F (2018) Evaluation on diabetic plantar pressure data-set employing auto-segmentation technologies. Neural Comput Appl.  https://doi.org/10.1007/s00521-018-3838-x Google Scholar
  7. 7.
    Chernyshov KR (2009) An information theoretic approach to neural network based system identification. In: International conference on control and communicationsGoogle Scholar
  8. 8.
    Cho J-H, Lin A, Wang K (2013) Kernel-based method for feature selection and disease diagnosis using transcriptomics data. J Syst Biomed 1:254CrossRefGoogle Scholar
  9. 9.
    Cohn D, Zuk O, Kaplan T (2018) Enhancer identification using transfer and adversarial deep learning of DNA sequences. bioRxiv preprint first posted online 13 Feb 2018. http://dx.doi.org/10.1101/26420
  10. 10.
    De Albuquerque VHC, Nunes TM, Pereira DR et al (2018) Robust automated cardiac arrhythmia detection in ECG beat signals. Neural Comput Appl 29:679.  https://doi.org/10.1007/s00521-016-2472-8 CrossRefGoogle Scholar
  11. 11.
    Das A, Kempe D (2011) Submodular meets spectral: greedy algorithms for subset selection, sparse approximation and dictionary selection. International conference on machine learningGoogle Scholar
  12. 12.
    Floares AG, Floares AG (2008) Artificial intelligence support for interferon treatment decision in chronic hepatitis B. World Acad Sci Eng Technol 44:110–115Google Scholar
  13. 13.
    Gheyas IA, Smith LS (2010) Feature subset selection in large dimensionality domains. Pattern Recogn 43:5–13CrossRefzbMATHGoogle Scholar
  14. 14.
    Ginn SL, Alexander IE, Edelstein ML, Abedi MR, Wixon J (2013) Gene therapy clinical trials worldwide to 2012: an update. J Gene Med 15(2):65–77.  https://doi.org/10.1002/jgm.2698 CrossRefGoogle Scholar
  15. 15.
    Griffiths AJF, Wessler SR, Carroll SB, Doebley J (2012) Introduction to genetic analysis, vol 10. W.H. Freeman and Company, New York, p 58Google Scholar
  16. 16.
    Gurovich Y, Hanani Y, Bar O, Fleischer N, Gelbman D, Basel-Salmon L, Krawitz P, Kamphausen SB, Zenker M, Bird LM, Gripp KW (2018) DeepGestalt: identifying rare genetic syndromes using deep learning. https://arxiv.org/pdf/1801.07637.pdf
  17. 17.
    Hassan M, Abdalla MI, Ahmed SR, Akil W, Esmat G, Khamis S, ElHefnaw M (2011) The decision tree mode for prediction the response to the treatment in patients with chronic hepatitis C. N Y Sci J 4(7):69–79Google Scholar
  18. 18.
    Inbarani HH, Kumar SS (2015) Hybrid tolerance rough set based intelligent approaches for social tagging systems. Big data in complex systems: challenges and opportunities. Stud Big Data 9(1):231–261CrossRefGoogle Scholar
  19. 19.
    Keane MG, Pyeritz RE (2008) Medical management of Marfan syndrome. Circulation. 117(21):2802–2813.  https://doi.org/10.1161/circulationaha.107.693523 CrossRefGoogle Scholar
  20. 20.
    King R, Karwath A, Clare A, Dehaspe L (2001) The utility of different representations of protein sequence for predicting functional class. Bioinformatics 17(5):445–454CrossRefGoogle Scholar
  21. 21.
    Kuliev A, Verlinsky Y (2005) Preimplantation diagnosis: a realistic option for assisted reproduction and genetic practice. Curr Opin Obstet Gynecol 17(2):179–183.  https://doi.org/10.1097/01.gco.0000162189.76349.c5 CrossRefGoogle Scholar
  22. 22.
    Kumar SS, Inbarani HH (2013) Analysis of mixed c-means clustering approach for brain tumour gene expression data. Int J Data Anal Tech Strateg 5(2):214–228CrossRefGoogle Scholar
  23. 23.
    Lindquist KJ et al (2013) The impact of improved microarray coverage and larger sample sizes on future genome-wide association studies. Genet Epidemiol 37(4):383–392CrossRefGoogle Scholar
  24. 24.
    Lopes CRS, Ludermir TB, de Souto MCP, Ludermir AB (2002) Neural networks for the analysis of common mental disorders factors. J Neural Netw. ISBN-0-7695-1709-9Google Scholar
  25. 25.
    Motsinger-Reif AA, Ritchie MD (2008) Neural networks for genetic epidemiology: past, present, and future. J Bio Data Min 1(3):1Google Scholar
  26. 26.
    Nunes TM, Coelho AL, Lima CA, Papa JP, de Albuquerque VHC (2014) EEG signal classification for epilepsy diagnosis via optimum path forest: a systematic assessment. Neurocomputing 136:103–123CrossRefGoogle Scholar
  27. 27.
    Pandey B, Ranjan S, Shukla A, Tiwari R (2010) Sentence recognition using Hopfield neural network. Int J Comput Sci 7(4):6Google Scholar
  28. 28.
    Panthonga R, Srivihok A (2015) Wrapper feature subset selection for dimension reduction based on ensemble learning algorithm. Proc Comput Sc 72:162–169CrossRefGoogle Scholar
  29. 29.
    Patro SGK, Sahu KK (2015) Normalization: a preprocessing stage. https://arxiv.org/ftp/arxiv/papers/1503/1503.06462.pdf
  30. 30.
    Peixoto SA, Rebouças Filho PP, Kumar NA, de Albuquerque VHC (2018) Automatic classification of pulmonary diseases using a structural co-occurrence matrix. Neural Comput Appl.  https://doi.org/10.1007/s00521-018-3736- Google Scholar
  31. 31.
    Peng Y, Wu Z, Jiang J (2010) A novel feature selection approach for biomedical data classification. J Biomed Inf 43:15–23CrossRefGoogle Scholar
  32. 32.
    Rattanakronkul N, Waiyamai K (2002) Combining association rule discovery and data classification for protein structure prediction. In: The international conference on bio-informaticsGoogle Scholar
  33. 33.
    Rebouças Filho PP, Cortez PC, da Silva Barros AC, De Albuquerque VHC (2014) Novel adaptive balloon active contour method based on internal force for image segmentation: a systematic evaluation on synthetic and real images. Expert Syst Appl 41(17):7707–7721CrossRefGoogle Scholar
  34. 34.
    Shakeel PM, Baskar S, Dhulipala VRS, Mishra S, Jaber MM (2018) Maintaining security and privacy in health care system using learning based deep-Q-networks. J Med Syst 42:186CrossRefGoogle Scholar
  35. 35.
    Shalabi LA, Shaaban Z, Kasasbeh B (2006) Data mining: a preprocessing engine. J Comput Sci 2:735–739CrossRefGoogle Scholar
  36. 36.
    Sridhar KP, Baskar S, Shakeel PM et al (2018) Developing brain abnormality recognize system using multi-objective pattern producing neural network. J Ambient Intell Human Comput.  https://doi.org/10.1007/s12652-018-1058-y Google Scholar
  37. 37.
    Wang ZY, Guo ZY, Huang LH, Liu XZ (2017) Dynamical behavior of complex-valued hopfield neural networks with discontinuous activation functions. Neural Process Lett 45(3):1039–1061CrossRefGoogle Scholar
  38. 38.
    Xu C, Li P (2017) Pseudo almost periodic solutions for high-order Hopfield neural networks with time-varying leakage delays. Neural Process Lett 46(1):41–58MathSciNetCrossRefGoogle Scholar
  39. 39.
    Yaacoub C, Mhanna G, Rihana S (2017) A genetic-based feature selection approach in the identification of left/right hand motor imagery for a brain–computer interface. J Brain Sci 7:12CrossRefGoogle Scholar
  40. 40.
    Ziarko W (2008) Probabilistic approach to rough sets. Int J Approx Reason Sci Direct 49(2):272MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science Department, Community CollegeKing Saud UniversityRiyadhSaudi Arabia

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