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A Probe into Performance Analysis of Real-Time Forecasting of Endemic Infectious Diseases Using Machine Learning and Deep Learning Algorithms

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Advanced Prognostic Predictive Modelling in Healthcare Data Analytics

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 64))

Abstract

The current work aims at probing the performance of real-time forecasting of endemic infectious diseases by means of machine learning and deep learning techniques. An LSTM-based time series forecasting framework and machine learning-based framework are proposed for forecasting the endemic infectious diseases in real time. With recent outbreaks of Ebola, Zika, cholera, and COVID 2019, a question is being raised on our alertness as well as preparedness toward controlling the spread of these pandemics. Accurate and reliable prediction occurrences of these diseases are compulsory for the health personals to enable timely response in handling these outbreaks. The diversities of the communities make it more complex along with the humongous data generated due to the convergence of SMAC technologies. The data generated due to this complex network is nonlinear and non-stationary. Processing of this data requires an effort from a multidimensional perspective. The current work proposed the utilization of machine learning and deep learning-based long short-term memory (LSTM) techniques for the assessment of time series forecasting of casualties in case of cholera outbreak that happened recently in Yemen. The feasibility of these two techniques is probed using performance evaluation metrics. The core objective of using these two techniques is in considering nonlinear and non-stationary behavior.

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References

  1. Pyne S, Vullikanti AKS, Marathe MV (2015) Big data applications in health sciences and epidemiology. Handb Stat 33:171–202. ISSN 0169-7161

    Google Scholar 

  2. https://en.wikipedia.org/wiki/Timeline_of_cholera

  3. Last J (2001) A dictionary of epidemiology, 4th edn. Oxford University Press, New York

    Google Scholar 

  4. Brauer F, van den Driessche P, Wu J (eds) (2008) Mathematical epidemiology. Lecture notes in mathematics 1945. Springer Verlag, Berlin

    Google Scholar 

  5. http://www.idc.com/research/Predictions13/downloadable/238044.pdf

  6. http://www.gartner.com/technology/research/nexus-of-forces/

  7. Pandey MK, Karthikeyan S (2017) Performance analysis of time series forecasting of ebola casualties using machine learning algorithm. In: Proceedings ITISE 201, Granada, 18–20 September 2017. ISBN 978-84-17293-01-7 Google Scholar

    Google Scholar 

  8. Pandey MK, Subbiah K (2019) Performance analysis of time series forecasting using machine learning algorithms for prediction of ebola casualties. Commun Comput Inform Sci 899:320–334. https://doi.org/10.1007/978-981-13-2035-4_28

    Article  Google Scholar 

  9. Pandey MK, Subbiah K (2016) Social networking and big data analytics assisted reliable recommendation system model for internet of vehicles. In: Hsu CH, Wang S, Zhou A, Shawkat A (eds) Internet of vehicles—technologies and services. IOV 2016. Lecture notes in computer science, vol 10036. Springer, Cham https://doi.org/10.1007/978-3-319-51969-2_13

  10. Pandey MK, Subbiah K (2016) A novel storage architecture for facilitating efficient analytics of health informatics big data in cloud. In: 2016 IEEE international conference on computer and information technology (CIT), Nadi, pp. 578–585. https://doi.org/10.1109/CIT.2016.86

  11. M. K. Pandey, S. Kumar, K. Subbiah, “Information Security Management System (ISMS) Standards in Cloud Computing-A Critical Review,” International Conference on Control, Computing, Communication and Materials, Allahabad, 2013. https://doi.org/10.13140/RG.2.1.3687.4649

  12. Kumar S, Kumar M (2014) Article: comparison of dynamic load balancing policies in data centers. Int J Comput Appl 104(17):9–13. https://doi.org/10.5120/18298-8324

    Article  Google Scholar 

  13. Kumar S, Pandey MK, Nath A, Subbiah K, Singh MK (2015) Comparative study on machine learning techniques in predicting the QoS-values for web-services recommendations. In: International conference on computing, communication and automation, Noida, pp 161–167. https://doi.org/10.1109/CCAA.2015.7148398

  14. Kumar S, Pandey MK, Nath A, Subbiah K (2015) Missing QoS-values predictions using neural networks for cloud computing environments. In: 2015 international conference on computing and network communications (CoCoNet), Trivandrum, pp 414–419. https://doi.org/10.1109/CoCoNet.2015.7411219

  15. Kumar S, Pandey MK, Nath A, Subbiah K (2016) Performance analysis of ensemble supervised machine learning algorithms for missing value imputation. In: 2016 2nd international conference on computational intelligence and networks (CINE), Bhubaneswar, pp 160–165. https://doi.org/10.1109/CINE.2016.35

  16. Singh VP, Pandey MK, Singh PS, Karthikeyan S (2019) An empirical mode decomposition (EMD) enabled long sort term memory (LSTM) based time series forecasting framework for web services recommendation. In: Frontiers in artificial intelligence and applications, vol 320. IOS Press, pp 715–723. https://doi.org/10.3233/FAIA190241

  17. Singh VP, Pandey MK, Singh PS, Karthikeyan S (2020) An econometric time series forecasting framework for web services recommendation. In: Procedia computer science, vol 167. Elsevier B.V, pp 1615–1625. https://doi.org/10.1016/j.procs.2020.03.372

  18. Singh VP, Pandey MK, Singh PS, Karthikeyan S (2020) Neural net time series forecasting framework for time-aware web services recommendation. Proc Comput Sci 171:1313–1322. https://doi.org/10.1016/j.procs.2020.04.140

    Article  Google Scholar 

  19. Singh VP, Pandey MK, Singh PS, Karthikeyan S (2020) An LSTM based time series forecasting framework for web services recommendation. Computación y Sistemas 24(2). https://doi.org/10.13053/cys-24-2-3402

  20. Mittal M, Balas VE, Goyal LM, Kumar R, In: Big data processing using spark in cloud, vol 43. Springer, Singapore. ISBN 9789811305498. https://doi.org/10.1007/978-981-13-0550-4

  21. Singh A, Mittal M, Kapoor N (2019) Data processing framework using apache and spark technologies in big data. In: Mittal M, Balas V, Goyal L, Kumar R (eds) Big data processing using spark in cloud. Studies in big data, vol 43. Springer, Singapore. https://doi.org/10.1007/978-981-13-0550-4_5

  22. Mittal M, Balas VE, Hemanth DJ, In: Data intensive computing applications for big data, vol 29, IOS Press. ISBN 9781614998136

    Google Scholar 

  23. Mittal A, Kumar D, Mittal M, Saba T, Abunadi I, Rehman A, Roy S (2020) Detecting pneumonia using convolutions and dynamic capsule routing for chest x-ray images. Sensors 20:1068

    Article  Google Scholar 

  24. Mittal M, Arora M, Pandey T, Goyal LM (2020) Image segmentation using deep learning techniques in medical images. In: Verma O, Roy S, Pandey S, Mittal M (eds) Advancement of machine intelligence in interactive medical image analysis. Algorithms for intelligent systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1100-4_3

  25. Kaur B, Sharma M, Mittal M, Verma A, Mohan Goyal L, Jude Hemanth D (2018) An improved salient object detection algorithm combining background and foreground connectivity for brain image analysis. Comput Electr Eng 71:692–703. ISSN 0045-7906. https://doi.org/10.1016/j.compeleceng.2018.08.018

  26. Dash S, Acharya BR, Mittal M, Abraham A, Kelemen A, In deep learning techniques for biomedical and health informatics, vol 68. Springer, Cham. ISBN 9783030339654, https://doi.org/10.1007/978-3-030-33966-1

  27. Bisset K, Chen J, Feng X, Vullikanti A, Marathe M (2009a) EpiFast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems. In: The proceeding of 23rd ACM international conference on supercomputing (ICS-09), ACM Press, New York

    Google Scholar 

  28. Salathé M et al (2012) Digital epidemiology. PLoS Comput Biol 8(7):e1002616

    Article  Google Scholar 

  29. Nsoesie EO, Brownstein JS, Ramakrishnan N, Marathe M (2013) A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza Other Respir Viruses 8(3):309–316

    Article  Google Scholar 

  30. Nishiura H (2011) Real-time forecasting of an epidemic using a discrete time stochastic model: a case study of pandemic influenza (H1N1-2009). BioMed Eng Online 10(1):15

    Article  Google Scholar 

  31. Ohkusa Y, Sugawara T, Taniguchi K, Okabe N (2011) Real-time estimation and prediction for pandemic A/H1N1(2009) in Japan. J Infect Chemother 17(4):468–472

    Article  Google Scholar 

  32. Hall IM, Gani R, Hughes HE, Leach S (2007) Real-time epidemic forecasting for pandemic influenza. Epidemiol Infect 135(3):372–385

    Article  Google Scholar 

  33. Tizzoni M, Bajardi P, Poletto C, Ramasco J, Balcan D, Goncalves B, Perra N, Colizza V, Vespignani A (2012) Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm. BMC Med 10(1):165. ISSN 1741-7015

    Google Scholar 

  34. Shaman J, Karspeck A (2012) Forecasting seasonal outbreaks of influenza. Proc Natl Acad Sci USA 109(50):20425–20430

    Article  Google Scholar 

  35. Shaman J, Goldstein E, Lipsitch M (2010) Absolute humidity and pandemic versus epidemic influenza. Am J Epidemiol 173(2):127–135

    Article  Google Scholar 

  36. Shaman J, Pitzer VE, Viboud C, Grenfell BT, Lipsitch M (2010) Absolute humidity and the seasonal onset of influenza in the continental United States. PLoS Biol 8(2):e1000316

    Article  Google Scholar 

  37. Chakraborty P, Khadivi P, Lewis B, Mahendiran A, Chen J, Butler P, Nsoesie EO, Mekaru SR, Brownstein JS, Marathe MV, Ramakrishnan N (2014) Forecasting a moving target: ensemble models for ILI case count predictions. In: Proceedings of the 2014 SIAM international conference on data mining, 28 April 2014, pp 262–270

    Google Scholar 

  38. Ramadona AL, Lazuardi L, Hii YL, Holmner Å, Kusnanto H, Rocklöv J (2016) Prediction of dengue outbreaks based on disease surveillance and meteorological data. PLoS ONE 11(3):e0152688. https://doi.org/10.1371/journal.pone.0152688

  39. Liao Y et al (2017) A new method for assessing the risk of infectious disease outbreak. Sci Rep 7:40084. https://doi.org/10.1038/srep40084

    Article  Google Scholar 

  40. Sharma S, Mangat V (2015) Relevance vector machine classification for big data on Ebola outbreak. In: 2015 1st international conference on next generation computing technologies (NGCT), Dehradun, pp 639–643. https://doi.org/10.1109/ngct.2015.7375199

  41. Marathe M (2015) Assisting H1N1 and Ebola outbreak response through high performance networked epidemiology. In: 2015 IEEE international parallel and distributed processing symposium, Hyderabad, India, pp 831–831. https://doi.org/10.1109/ipdps.2015.121

  42. Ristic B, Dawson P (2016) Real-time forecasting of an epidemic outbreak: Ebola 2014/2015 case study. In: 2016 19th international conference on information fusion (FUSION), Heidelberg, pp 1983–1990

    Google Scholar 

  43. Buendia RJM, Solano GA (2015) A disease outbreak detection system using autoregressive moving average in time series analysis. In: 2015 6th international conference on information, intelligence, systems and applications (IISA), Corfu, pp 1–5. https://doi.org/10.1109/iisa.2015.7388087

  44. Wang Y, Gu J (2015) A hybrid prediction model applied to diarrhea time series. In: 2015 12th international conference on fuzzy systems and knowledge discovery (FSKD), Zhangjiajie, pp 1096–1102. https://doi.org/10.1109/fskd.2015.7382095

  45. https://data.humdata.org/dataset/yemen-cholera-outbreak-daily-epidemiology-update

  46. Bishop CM (2006) Pattern recognition and machine learning. Springer, New Yor

    MATH  Google Scholar 

  47. Kingma, Diederik, and Jimmy Ba. “Adam: A method for stochastic optimization.” arXiv preprint arXiv: 1412.6980 (2014)

    Google Scholar 

  48. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  49. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256

    Google Scholar 

  50. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034

    Google Scholar 

  51. Saxe AM, McClelland JL, Ganguli S (2013) Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv preprint arXiv: 1312.6120

    Google Scholar 

  52. Murphy KP (2012) Machine learning: a probabilistic perspective. The MIT Press, Cambridge, Massachusetts

    MATH  Google Scholar 

  53. Pascanu R, Mikolov T, Bengio Y (2013) On the difficulty of training recurrent neural networks. In: Proceedings of the 30th international conference on machine learning, vol 28(3), pp 1310–1318

    Google Scholar 

  54. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11:10–18

    Article  Google Scholar 

  55. Shevade SK, Keerthi SS, Bhattacharyya C, Murthy KRK (1999) Improvements to the SMO algorithm for SVM regression. In: IEEE transactions on neural networks

    Google Scholar 

  56. Smola AJ, Schoelkopf B (1998) A tutorial on support vector regression

    Google Scholar 

  57. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    MATH  Google Scholar 

  58. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, 221005, India

    Manish Kumar Pandey & Prashant K. Srivastava

Authors
  1. Manish Kumar Pandey
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  2. Prashant K. Srivastava
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Correspondence to Manish Kumar Pandey .

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Editors and Affiliations

  1. Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA

    Sudipta Roy

  2. Department of Computer Engineering, J. C. Bose University of Science and Technology, YMCA, Faridabad, India

    Lalit Mohan Goyal

  3. Department of Computer Science and Engineering, G. B. Pant Government Engineering College, New Delhi, India

    Mamta Mittal

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Pandey, M.K., Srivastava, P.K. (2021). A Probe into Performance Analysis of Real-Time Forecasting of Endemic Infectious Diseases Using Machine Learning and Deep Learning Algorithms. In: Roy, S., Goyal, L.M., Mittal, M. (eds) Advanced Prognostic Predictive Modelling in Healthcare Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 64. Springer, Singapore. https://doi.org/10.1007/978-981-16-0538-3_12

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