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Machine Learning Toward Infectious Disease Treatment

  • Tulika Bhardwaj
  • Pallavi Somvanshi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

The emergence of infectious diseases poses a serious threat to human and animal health. This is evident from the sudden cases of hospital outbreaks due to the surveillance and evolution of zoonotic pathogens. The main reason is the development of resistance mechanism in infectious pathogens against broad-spectrum drugs. This leads to the mortality rate of 69% due to infectious diseases at global level. Although improvements have been made at next-generation epidemiological study level to combat such issues but shortfall still observed due to the gap between patients and governmental authorities assigned for treatment. Additionally, handling, analyzing and updating of large datasets is time consuming and labor intensive. To overcome such limitations, applied informatics was employed for the sorting of multipart disciplines of research and pathogenesis identification and treatment. To understand the underlying problem, mining of the diagnostic techniques was performed focused to execute the correct disease diagnosis in different symptoms from the patient. Data preprocessing enables improvement quality of data as redundant data requires continuous discrete mining for analysis. Graphical interfaces were utilized for the comparative analysis of the problem in n number of ways by random decisions and tree making processes. Categorization of the single problem into supervised, unsupervised and weakly supervised principles offers a complete set of appropriate outputs directed towards disease treatment. The whole process favors data preprocessing, data mining, and data analysis by employing various machine learning approaches, data interpretation by statistical platforms and data visualization. The complete chapter reviewed challenges, pathway, and opportunities provided by machine learning approaches toward infectious disease treatments.

Keywords

Support vector machine Fuzzy logic Artificial neural network Infectious disease Machine learning 

Notes

Ethics Approval and Consent to Participate

Not Applicable

References

  1. 1.
    Schaepe, K.S.: Bad news and first impressions: patient and family caregiver accounts of learning the cancer diagnosis. Social Sci. Med. (1982) 73(6), 912–921 (2011)Google Scholar
  2. 2.
    Muir, P., Li, S., Lou, S., Wang, D., Spakowicz, D.J., Salichos, L., et al.: The real cost of sequencing: scaling computation to keep pace with data generation. Genome Biol. 17, 53 (2016)CrossRefGoogle Scholar
  3. 3.
    Marblestone, A.H., Wayne, G., Kording, K.P.: Toward an Integration of Deep Learning and Neuroscience. Front. Comput. Neurosci. 10, 94 (2016)CrossRefGoogle Scholar
  4. 4.
    Siebert, J.C., Wagner, B.D., Juarez-Colunga, E.: Integrating and mining diverse data in human immunological studies. Bioanalysis 6(2), 209–223 (2014)CrossRefGoogle Scholar
  5. 5.
    Zhao, Y., Healy, B.C., Rotstein, D., Guttmann, C.R.G., Bakshi, R., Weiner, H.L., et al.: Exploration of machine learning techniques in predicting multiple sclerosis disease course. PLoS ONE 12(4), e0174866 (2017)CrossRefGoogle Scholar
  6. 6.
    Li, Y., Lenaghan, S.C., Zhang, M.: A data-driven predictive approach for drug delivery using machine learning techniques. PLoS ONE 7(2), e31724 (2012)CrossRefGoogle Scholar
  7. 7.
    Gibbons, C., Richards, S., Valderas, J.M., Campbell, J.: Supervised machine learning algorithms can classify open-text feedback of doctor performance with human-level accuracy. J. Med. Internet Res. 19(3), e65 (2017)CrossRefGoogle Scholar
  8. 8.
    Bhaskar, H., Hoyle, D.C., Singh, S.: Machine learning in bioinformatics: a brief survey and recommendations for practitioners. Comput. Biol. Med. 36, 1104–1125 (2006)CrossRefGoogle Scholar
  9. 9.
    Muldrew, K.L.: Molecular diagnostics of infectious diseases. Curr. Opin. Pediatr. 21(1), 102–111 (2009)CrossRefGoogle Scholar
  10. 10.
    Rweyemamu, M., Kambarage, D., Karimuribo, E., et al.: Development of a one health national capacity in Africa: the Southern African centre for infectious disease surveillance (SACIDS) one health virtual centre model. Curr. Topics Microbiol. Immunol. 366, 73–91 (2013)Google Scholar
  11. 11.
    Caliendo, A.M., Gilbert, D.N., Ginocchio, C.C., et al.: Tests, better care: improved diagnostics for infectious diseases. Clin. Infect. Dis. 57(3), 139–170 (2013)CrossRefGoogle Scholar
  12. 12.
    Calmy, N.F., Hirschel, B., et al.: HIV viral load monitoring in resource-limited regions: optional or necessary? Clin. Infect. Diseases 44(1), 128–134 (2007)Google Scholar
  13. 13.
    Pereira, C.F., Paridaen, J.T.: Anti-HIV drug development—an overview. Curr. Pharm. Des. 10(32), 4005–4037 (2004)CrossRefGoogle Scholar
  14. 14.
    Collett, M.S., Neyts, J., Modlin, J.F.: A case for developing antiviral drugs against polio 79(3), 179–87 (2008)Google Scholar
  15. 15.
    Altunaiji, S., Kukuruzovic, R., Curtis, N., Massie, J.: Antibiotics for whooping cough (pertussis).Cochrane Database Syst. Rev. 1, CD004404 (2005)Google Scholar
  16. 16.
    Plemper, R. K., Snyder, J. P.: Measles control—can measles virus inhibitors make a difference? Curr. Opin. Investig. Drugs (London, England: 2000) 10(8), 811–820 (2009)Google Scholar
  17. 17.
    Swindells, S.: New drugs to treat tuberculosis. F1000 Med. Rep. 4,12 (2012)Google Scholar
  18. 18.
    Klein, E.Y.: Antimalarial drug resistance: a review of the biology and strategies to delay emergence and spread. Int. J. Antimicrob. Agents 41(4), 311–317 (2013)CrossRefGoogle Scholar
  19. 19.
    Bryant, J., Chewapreecha, C., Bentley, S.D.: Developing insights into the mechanisms of evolution of bacterial pathogens from whole-genome sequences. Future Microbiol. 7(11), 1283–1296 (2012)CrossRefGoogle Scholar
  20. 20.
    Davies, J., Davies, D.: Origins and evolution of antibiotic resistance. Microbiol. Mol. Biol. Rev. MMBR 74(3), 417–433 (2010)CrossRefGoogle Scholar
  21. 21.
    Bhardwaj, T., Somvanshi, P.: Pan-genome analysis of clostridium botulinum reveals unique targets for drug development. Gene 623, 48–62 (2017)CrossRefGoogle Scholar
  22. 22.
    Venancio, T.M., Bellieny-Rabelo, D., Aravind, L.: Evolutionary and biochemical aspects of chemical stress resistance in Saccharomyces cerevisiae. Front. Genet. 3, Article 47 (2012)Google Scholar
  23. 23.
    Khan, S., Somvanshi, P., Bhardwaj, T., Mandal, R.K., Dar, S.A., et al.: Aspartate-β-semialdeyhyde dehydrogenase as a potential therapeutic target of Mycobacterium tuberculosis H37Rv: evidence from in silico elementary mode analysis of biological network model. J. Cell. Biochem. 119(3), 2832–2842 (2018).  https://doi.org/10.1002/jcb.26458CrossRefGoogle Scholar
  24. 24.
    Meyer, W.G., Pavlin, J.A., Hospenthal, D., et al.: Antimicrobial resistance surveillance in the AFHSC-GEIS network. BMC Public Health 11(2) Article 8 (2011)Google Scholar
  25. 25.
    Fauci, A.S., Morens, D.M.: The perpetual challenge of infectious diseases. N. Engl. J. Med. 366(5), 454–461 (2012)CrossRefGoogle Scholar
  26. 26.
    Osama, K., Mishra, B.N., Somvanshi, P.: Machine Learning Techniques in Plant Biology. The Omics of Plant Science. Springer Publications, Plant Omics (2015)Google Scholar
  27. 27.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, Fourth Indian Reprint. Pearson Education, Singapore (2003)Google Scholar
  28. 28.
    Ghumbre, S., Patil, C., Ghatol, A.: Heart disease diagnosis using support vector machine. In: Proceedings of the International Conference on Computer Science and Information Technology (ICCSIT ‘11), Pattaya, Thailand (2011)Google Scholar
  29. 29.
    Bhatia, S., Prakash, P., Pillai, G.N.: SVM based decision support system for heart disease classification with integer-coded genetic algorithm to select critical features. In: Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA, pp. 34–38 (2008)Google Scholar
  30. 30.
    Xiaoqing, G., Ni, T., Wang, H.: New fuzzy support vector machine for the class imbalance problem in medical datasets classification. Sci. World J. 2014 (2014)Google Scholar
  31. 31.
    Janaradanan, P., Heena, L., Sabika, F.: effectiveness of support vector machines in data mining. J. Commun. Softw. Syst. 11(1) (2015)Google Scholar
  32. 32.
    Karim, M.N., Yoshida, T., Rivera, S.L., Saucedo, V.M., Eikens, B., Oh, G.-S.: Global and local neural network models in biotechnology: application to different cultivation processes. J. Ferment. Bioeng. 83(1), 1–11 (1997)CrossRefGoogle Scholar
  33. 33.
    Krenker, A., Bešter, J., Ko, A.: Introduction to the arti- ficial neural networks. In: Suzuki, K. (ed.) Artificial Neural Networks-Methodological Advances and Biomedical Applications, pp. 3–18. Carotia, Intech, Rijeka (2011)Google Scholar
  34. 34.
    Widrow, B., Hoff, M.: Adaptive switching circuits. 1960 IRE WESCON convention record, vol. 4, pp. 96–104. IRE, New York (1960)Google Scholar
  35. 35.
    Prasad, V., Gupta, S.D.: Applications and potentials of artificial neural networks in plant tissue culture. In: Gupta, S.D., Ibaraki, Y. (eds.) Plant Tissue Culture Engineering, pp. 47–67. Springer, Netherlands (2006)Google Scholar
  36. 36.
    Mandic, D.P., Chambers, J.: Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability. Wiley, Chichester/New York (2001)CrossRefGoogle Scholar
  37. 37.
    Yang, Z.R.: A novel radial basis function neural network for discriminant analysis. IEEE Trans. Neural Netw. 17, 604–612 (2006)CrossRefGoogle Scholar
  38. 38.
    Li, C.Y., Liang, G.Y., Yao, W.Z., et al.: Integrated analysis of long non-coding RNA competing interactions reveals the potential role in progression of human gastric Cancer. Int. J. Oncol. 248, 1965–1976 (2016)Google Scholar
  39. 39.
    Stevens, R.H., Lopo, A.C.: Artificial neural network comparison of expert and novice problem-solving strategies. In: Proceedings of the Annual Symposium on Computer Application in Medical Care, pp. 64–68 (1994)Google Scholar
  40. 40.
    El-Solh, A.A., Hsiao, C.B., Goodnough, S., Serghani, R.N.J., Grant, B.J.B.: Predicting active pulmonary tuberculosis using an artificial neural network. Chest 116, 968–973 (1999)CrossRefGoogle Scholar
  41. 41.
    Narain, R., Saxena, S., Goyal, A.K.: Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach. Patient Prefer. Adher. 10, 1259–1270 (2016)CrossRefGoogle Scholar
  42. 42.
    Anagnostou, T., Remzi, M., Djavan, B.: Artificial neural networks for decision-making in urologic oncology. Rev. Urol. 5(1), 15–21 (2003)Google Scholar
  43. 43.
    Cordes, J.S., Mathiak, K.A., Dyck, M., Alawi, E.M., Gaber, T.J., Zepf, F.D., et al.: Cognitive and neural strategies during control of the anterior cingulate cortex by fMRI neurofeedback in patients with schizophrenia. Front. Behav. Neurosci. 9, 169 (2015)CrossRefGoogle Scholar
  44. 44.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefGoogle Scholar
  45. 45.
    Zaitsev, D.A., Sarbei, V.G., Sleptsov, A.I.: Synthesis of continuous-valued logic functions defined in tabular form. Cybern. Syst. Anal. 34(2), 190–195 (1998)CrossRefGoogle Scholar
  46. 46.
    Prihatini, P.M., Putra, I.K.G.D.: Fuzzy knowledge-based system with uncertainty for tropical infectious disease diagnosis. IJCSI Int. J. Comput. Sci. Issues 9(4), 3 (2012)Google Scholar
  47. 47.
    Zarandi, F.M.H., Zolnoori, M., Moin, M., Heidarnejad, H.: A fuzzy rule-based expert system for diagnosing asthma. Trans. E. Ind. Eng. 17, 129–142 (2010)Google Scholar
  48. 48.
    Razak, T.R.B., Ramli, M.H., Wahab, R.A.: Dengue notification system using fuzzy logic. In: 2013 International Conference on Computer, Control, Informatics and Its Applications (2013)Google Scholar
  49. 49.
    Gago, J., Landín, M., Gallego, P.P.: Artificial neural networks modeling the in vitro rhizogenesis and acclimatization of Vitis vinifera L. J. Plant Physiol. 167, 1226–1231 (2010)CrossRefGoogle Scholar
  50. 50.
    Goswami, N.D., Pfeiffer, C.D., Horton, J.R., Chiswell, K., Tasneem, A., Tsalik, E.L.: The state of infectious diseases clinical trials: a systematic review of clinicaltrials.gov. PLoS ONE 8(10), e77086 (2013)CrossRefGoogle Scholar
  51. 51.
    Wang, Y., Gu, J., Zhou, Z.: Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai 2015. Appl. Soft Comput. 280–290 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of BiotechnologyTERI School of Advanced StudiesNew DelhiIndia

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