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Contagious Diseases Prediction in Healthcare Over Big Data

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Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018) (ICCBI 2018)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 31))

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Abstract

As Data trends go higher and higher, the data creation process tends to increase at an unprecedented rate. They are not only increasing, they also change, and incarnate from one form to the another. Recently, Big data revolution has introduced an incarnation of how the data is captured, accessed, aggregated, converted and stored. Healthcare communities is one of the big data procreations, where data gets generated rapidly. The appropriate analysis of healthcare data helps to detect the diseases at an early stage to save the patients’ life. Although, analysis is required to make a proper decision, this analysis is declined due to the incomplete data. This paper focuses on the prediction towards the emerging contagious diseases to provide enhanced accuracy in the process of disease detection. To solve the incomplete e.g. missing data, the Improved Expectation Maximization (IEM) has been employed. Here, the experiment is carried out on Ebola disease. This research work proposed a new Ensemble Neural Network algorithm using structured and unstructured data. A proposed model was trained on prediction from dataset and evaluate each model. In this work, the Ensemble Neural Network shows an average performance, but leverages a very firm and more consistent performance analysis accuracy. This method will deliver an enhanced potential for the decision makers in healthcare communities.

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References

  1. WHO: Roll back malaria. Malaria early warning system. A Framework for Field Research in Africa: concepts, indicators and partners. Geneva: World Health Organization (2001)

    Google Scholar 

  2. WHO: Prevention and control of malaria epidemics. Third meeting of the Technical Support Network. World Health Organization, Geneva (2002)

    Google Scholar 

  3. Watkins, R.E., Eagleson, S., Hall, R.G., Dailey, L., Plant, A.J.: Approaches to the evaluation of outbreak detection methods. BMC Public Health 6, 263 (2006)

    Article  Google Scholar 

  4. Mckelvie, W.R., Haghdoost, A.A., Raeisi, A.: Defining and detecting malaria epidemics in south-east Iran. Malar J. 11, 81 (2012)

    Article  Google Scholar 

  5. World Health Organization: New horizons for health through mobile technologies (2011)

    Google Scholar 

  6. Randrianasolo, L., Raoelina, Y., Ratsitorahina, M., Ravolomanana, L., Andria-mandimby, S., Heraud, J., et al.: Sentinel surveillance system for early outbreak detection in Madagascar. BMC Public Health 10, 31 (2010)

    Article  Google Scholar 

  7. WHO: Roll Back Malaria. Disease surveillance for malaria elimination. World Health Organization, Geneva (2012)

    Google Scholar 

  8. Brady, O.J., Smith, D.L., Scott, T.W., Hay, S.I.: Dengue disease outbreak definitions are implicitly variable. Epidemics 11, 92–102 (2015)

    Article  Google Scholar 

  9. CDC: Framework for evaluating public health surveillance systems for early detection of outbreaks recommendations from the CDC Working Group (2004)

    Google Scholar 

  10. Chen, M., Ma, Y., Li, Y., Wu, D., Zhang, Y., Youn, C.: Wearable 2.0: enable human-cloud integration in next generation healthcare system. IEEE Commun. 55(1), 54–61 (2017)

    Article  Google Scholar 

  11. Chen, M., Ma, Y., Song, J., Lai, C., Hu, B.: Smart clothing: connecting human with clouds and big data for sustainable health monitoring. Mob. Netw. Appl. 21(5), 82 (2016)

    Article  Google Scholar 

  12. Chen, M., Zhou, P., Fortino, G.: Emotion communication system. IEEE Access. https://doi.org/10.1109/access.2016.2641480 (2016)

  13. Qiu, M., Sha, E.H.-M.: Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems. ACM Trans. Des. Autom. Electron. Syst. (TODAES) 14(2), 25 (2009)

    Google Scholar 

  14. Wang, J., Qiu, M., Guo, B.: Enabling real-time information service on telehealth system over cloud-based big data platform. J. Syst. Architect. 72, 69–79 (2017)

    Article  Google Scholar 

  15. Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., Escobar, G.: Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff. 33(7), 1123–1131 (2014)

    Article  Google Scholar 

  16. Qiu, L., Gai, K., Qiu, M.: Optimal big data sharing approach for tele-health in cloud computing. In: IEEE International Conference on Smart Cloud (SmartCloud), pp. 184–189. IEEE (2016)

    Google Scholar 

  17. Zhang, Y., Qiu, M., Tsai, C.-W., Hassan, M.M., Alamri, A.: HealthCPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst. J. (2015)

    Google Scholar 

  18. Lin, K., Luo, J., Hu, L., Hossain, M.S., Ghoneim, A.: Localization based on social big data analysis in the vehicular networks. IEEE Trans. Ind. Inf. (2016)

    Google Scholar 

  19. Lin, K., Chen, M., Deng, J., Hassan, M.M., Fortino, G.: Enhanced fingerprinting and trajectory prediction for iot localization in smart buildings. IEEE Trans. Autom. Sci. Eng. 13(3), 1294–1307 (2016)

    Article  Google Scholar 

  20. Marcoon, S., Chang, A.M., Lee, B., Salhi, R., Hollander, J.E.: Heart score to further risk stratify patients with low timi scores. Crit. Pathways Cardiol. 12(1), 1–5 (2013)

    Article  Google Scholar 

  21. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. JRSSB 39, 1–38 (1997)

    MathSciNet  Google Scholar 

  22. Gakwaya, N.J., Manju Priya, S.: Improved ant colony on feature selection and weighted ensemble neural network based multimodal disease risk prediction classifier for disease prediction over big data. Int. J. Eng. Technol. 56–61 (2018)

    Google Scholar 

  23. Gakwaya, N.J., Manju Priya, S.: Improved malaria prediction using ant colony feature selection and random forest tree in big data analytics. J. Adv. Res. Dyn. Control Syst. 10(12-Special Issue) (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science, Karpagam Academy of Higher Education, Coimbatore, India

    Nkundimana Joel Gakwaya & S. Manju Priya

Authors
  1. Nkundimana Joel Gakwaya
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  2. S. Manju Priya
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Corresponding author

Correspondence to Nkundimana Joel Gakwaya .

Editor information

Editors and Affiliations

  1. Department of CSE, Vaigai College of Engineering, Melur, India

    A. Pasumpon Pandian

  2. Department of Engineering, University of the Ryukyus, Okinawa, Japan

    Tomonobu Senjyu

  3. Edith Cowan University (ECU), Joondalup, WA, Australia

    Syed Mohammed Shamsul Islam

  4. Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA

    Haoxiang Wang

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Gakwaya, N.J., Manju Priya, S. (2020). Contagious Diseases Prediction in Healthcare Over Big Data. In: Pandian, A.P., Senjyu, T., Islam, S.M.S., Wang, H. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018). ICCBI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-24643-3_14

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  • DOI: https://doi.org/10.1007/978-3-030-24643-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24642-6

  • Online ISBN: 978-3-030-24643-3

  • eBook Packages: EngineeringEngineering (R0)

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