Survival Prediction of Myocardial Infarction Disease using Cloud Assistance
DOI:
https://doi.org/10.26438/ijcse/v6i10.140143Keywords:
E-healthcare system, Authentication, Cloud, Myocardial infarction, Gaussian naïve Bayes classifier, algorithm, survival predictionAbstract
E-healthcare system have been increasingly facilitating health condition monitoring, early intervention and evidence based medical treatment by accepting the Personal Health Information (PHI) of the patients. Myocardial Infarction is one of the most leading cause variations in the health condition and that can be lead to the death of the human being. The early prediction of such disease can reduce or prevent development of it and helps to take the necessary treatment. The proposed system is one efficient tool to predicting such diseases. This system can learn from the past data of those patients to be capable of predicting the survival of death of the patient with myocardial infarction. The health information data of the patients who are suffering from such disease is collected and stored. It consists survival period and some clinical data of patients who suffered from myocardial infarction can be used to train an intelligent system to predict the survival or death of current myocardial infarction patients. The Gaussian Naïve Bayes algorithms are used to train the collected data of patients and generalize the survival or death of current patients suffering from myocardial infarction. Experimentally, the instances are stored in the cloud and used as the trained instance. The test data will be provided by the physician to predict the survival or death of the current patient suffering from myocardial infarction.
References
[1] Jun Zhou, Zhenfu Cao, Senior Member, IEEE, Xiaolei Dong, and Xiaodong Lin, Senior Member, IEEE, “PPDM: A Privacy-Preserving Protocol for Cloud-Assisted e-Healthcare Systems”, IEEE journal of selected topics in signal processing vol 9, no 7, October 2015.
[2] M. Lichman, UCI Machine Learning Repository [http://archive.ics.uci.edu/ml] Irvine, CA: University of California, School of Information and Computer Science. 2013.
[3] Abdulkader Helwan, Dilber Uzun Ozsahin, Rahib Abiyev, John Bush, “One-Year Survival Prediction of Myocardial Infarction, (IJACSA) International Journal of Advanced Computer Science and Applications, vol 8, no 6, 2017.
[4] J. Zhou, Z. Cao, X. Dong, and X. Lin, “TR-MABE: White-box traceable and revocable multi-authority attribute-based encryption and its applications to multi-level privacy-preserving e-healthcare cloud computing systems,” in Proc. IEEE INFOCOM, 2015.
[5] J. Zhou, X. Lin, X. Dong, and Z. Cao, “PSMPA: Patient self-controllable and multi-level privacy-preserving cooperative authentication in distributed m-healthcare cloud computing system,” IEEE Trans. Parallel Distrib. Syst., to be published.
[6] L. Gatzoulis and L.lakovidis, “ wearable and portable E-health systems”, IEEE Eng. Med. Biol., MF., vol 26, no. 5, pp. 51-56, sep-oct. 2007.
[7] AS. Mullasari, P. Balaji, and T. Khando, “Managing complications in acute myocardial infarction”, Journal of the Association of Physicians of India 59, pp. 43-8, 2011.
[8] BW. Karlson, J. Herlitz, O. Wiklund, A. Richter, and A. Hjalmarson, “Early prediction of acute myocardial infarction from clinical history, examination and electrocardiogram in the emergency room”. The American journal of cardiology 68 (2), pp. 171-175, 1991.
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