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A Novel Software to Improve Healthcare Base on Predictive Analytics and Mobile Services for Cloud Data Centers

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Big Data and Networks Technologies (BDNT 2019)

Abstract

The main idea of this work is generated from studying the main challenges in the healthcare field. The goal is to identify patients who will be admitted to the hospital within the next year by using historical claims data including Information about patient and analysis it to solve the problem. But this work is dealing with very huge databases. Therefore, the new challenge is occurred the time required for executing the prediction methods and analysis, it bases on five error predicate measures, it including (Maximum error, RMSE, MSE, MAE and MAPE). As a result, cloud is suggested as a tool to solve the problem of analysing the predictions of a huge health care database. A new predictor is proposed to determine how many days in the next year a patient will spend in the hospital. In this work, we attempt to satisfy the idea that explains in Fig. 1. The main challenge here, how we can build the predictor that satisfies the gold triangle. The traversal of the T-Graph) cloud computing “i.e., Speed execution”, data mining algorithm “i.e., Abilities to deal with very huge databases” and Predication techniques” i.e., Ability to plan of the next years.

The idea and objective of this proposal

Flow chart of research work activities

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References

  1. National Center for Health Statistics, Health, United States, 2012: With Special Feature on Emergency Care, p. 342, Table 113

    Google Scholar 

  2. National Vital Statistics Reports, vol. 61(4) 8 May 2013, p. 81, Table 8

    Google Scholar 

  3. HPN. 2011. The heritage health prize competition. http://www.heritagehealthprize.com

  4. Al_Janabi, S., Abaid Mahdi, M.: Evaluation Prediction Techniques to Achievement an Optimal Biomedical Analysis, International Journal of Grid and Utility Computing (2019)

    Google Scholar 

  5. Duana, L., Streeta, W.N., Xu, E.: Healthcare information systems: data mining methods in the creation of a clinical recommender system. Enterp. Inf. Syst. 5(2), 169–181 (2011)

    Article  Google Scholar 

  6. Santhanam, T., Sundaram, S.: Application of CART algorithm in blood donors classification. J. Comput. Sci. 6(5), 548–552 (2011)

    Article  Google Scholar 

  7. Peng, X., Wu, W., Xu, J.: Leveraging machine learning in improving healthcare, Association for the Advancement of Artificial Intelligence (2011)

    Google Scholar 

  8. Soni, J., Ansari, U., Sharma, D., Soni, S.: Predictive data mining for medical diagnosis: an overview of heart disease prediction. Int. J. Comput. Appl. 17(8), 975–8887 (2011)

    Google Scholar 

  9. Sut, N., Simsek, O.: Comparison of regression tree data mining methods for prediction of mortality inhead injury. Expert Syst. Appl. 38(12), 15534–15539 (2011). Elsevier

    Article  Google Scholar 

  10. Rahman, R.M., Hasan, FRMd: Using and comparing different decision tree classification techniques for mining ICDDRB hospital surveillance data. Expert Syst. Appl. 38(9), 11421–11436 (2011). Elsevier

    Article  Google Scholar 

  11. Moon, S.S., Kang, S.-Y., Jitpitaklert, W., Kim, S.B.: Decision tree models for characterizing smoking patterns of older adults. Expert Syst. Appl. 39(1), 445–451 (2012)

    Article  Google Scholar 

  12. Zhang, J., Goode, K.M., Rigby, A., Balk, H.M.M., Cleland, J.G.: Identifying patients at risk of death or hospitalization due to worsening heart failure using decision tree analysis. Int. J. Cardiol. 163(2), 149–156 (2013). Elsevier

    Article  Google Scholar 

  13. Al-Janabi, S., Alkaim, A.F.: Springer, Soft Computing Journal (2019). https://doi.org/10.1007/s00500-019-03972-x

  14. Wu, X., Kumar, V. and others, Top 10 algorithms in data mining, Knowl. Inf. Syst. (2008)

    Google Scholar 

  15. Timofeev, R.: Classification and Regression Trees (CART) Theory and Applications Master thesis, Humboldt University, Berlin (2004)

    Google Scholar 

  16. Al-Janabi, S.: Pragmatic miner to risk analysis for intrusion detection (PMRA-ID). In: Mohamed, A., Berry, M.W., Yap, B.W. (eds.) SCDS 2017. CCIS, vol. 788, pp. 263–277. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-7242-0_23

    Chapter  Google Scholar 

  17. Ali, S.H.: Novel approach for generating the key of stream cipher system using random forest data mining algorithm, IEEE, 2013 Sixth International Conference on Developments in eSystems Engineering, Abu Dhabi, 2013, pp. 259–269. https://doi.org/10.1109/dese.2013.54

  18. Kalajdzic, K., Hussein Ali, S., Patel, A.: Rapid lossless compression of short text messages. Comput. Stan. Interfaces 37, 53–59 (2015). https://doi.org/10.1016/j.csi.2014.05.005. ISSN 0920-5489

    Article  Google Scholar 

  19. Kursa, M.B.: Robustness of the Random Forest-based gene selection methods (2013)

    Google Scholar 

  20. Friedman, J.H.: Multivariate Adaptive Regression Splines, Tech Report (1990)

    Google Scholar 

  21. Al-Janabi, S., Patel, A., Fatlawi, H., Kalajdzic, K., Al Shourbaji, I.: Empirical rapid and accurate prediction model for data mining tasks in cloud computing environments, IEEE, 2014 International Congress on Technology, Communication and Knowledge (ICTCK), pp. 1–8, Mashhad (2014). https://doi.org/10.1109/ictck.2014.7033495

  22. Jun, S.-H.: Boosted regression trees and random forests, Statistical Consulting Report (2013)

    Google Scholar 

  23. Schonlau Rand, M.: Boosted regression (boosting): an introductory tutorial and a Stata plugin, The Stata Journal (2005)

    Google Scholar 

  24. Ali, S.H.: A novel tool (FP-KC) for handle the three main dimensions reduction and association rule mining, IEEE, 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), Sousse, pp. 951–961 (2012). https://doi.org/10.1109/setit.2012.6482042

  25. Al_Janabi, S., Al_Shourbaji, I., Salman, M.A.: Assessing the suitability of soft computing approaches for forest fires prediction. Appl. Comput. Inform. 14(2), 214–224 (2018). https://doi.org/10.1016/j.aci.2017.09.006

    Article  Google Scholar 

  26. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian Network Classifiers, Machine Learning, vol. 29. Kluwer Academic Publishers, The Netherlands (1997)

    MATH  Google Scholar 

  27. Xiong, W., Wang, C.: A hybrid improved ant colony optimization and random forests feature selection method for 56 v/’ microarray data. IEEE Computer Society, Fifth International Joint Conference on INC, IMS and IDC (2009)

    Google Scholar 

  28. Al_Janabi, S.: Smart system to create optimal higher education environment using IDA and IOTs, International Journal of Computers and Applications, Taylor & Francis (2018). https://doi.org/10.1080/1206212x.2018.1512460

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Correspondence to Samaher Al_Janabi .

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Mahdi, M.A., Al_Janabi, S. (2020). A Novel Software to Improve Healthcare Base on Predictive Analytics and Mobile Services for Cloud Data Centers. In: Farhaoui, Y. (eds) Big Data and Networks Technologies. BDNT 2019. Lecture Notes in Networks and Systems, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-030-23672-4_23

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