An Efficient Classification Approach for Examine Health Records of Cause of Death

International Journal of Computer Science and Engineering

© 2017 by SSRG - IJCSE Journal

Volume 4 Issue 8

Year of Publication : 2017

Authors : Simma Narendra Prasad, Konni Srinivasa Rao

10.14445/23488387/IJCSE-V4I8P104

How to Cite?

Simma Narendra Prasad, Konni Srinivasa Rao, "An Efficient Classification Approach for Examine Health Records of Cause of Death," SSRG International Journal of Computer Science and Engineering , vol. 4,  no. 8, pp. 15-18, 2017. Crossref, https://doi.org/10.14445/23488387/IJCSE-V4I8P104

Abstract:

Now a day’s examining the health of each person in the every country is an integral part of healthcare. After examining the health of each person we can identify type of risk to be occurred. The analysis of risk based unlabelled data can be done by using classification approach in the data mining. Particularly we are take unlabelled data contains information related to participants in the health examination whose health condition is vary from great health to very ill. In this study we formulated the task of risk prediction as a multi-class classification problem using the Cause of Death (COD) information as labels, regarding the health-related death as the “highest risk”. The goal of risk prediction is to effectively classify 1) whether a health examination participant is at risk, and if yes, 2) predict what the key associated disease category is. In other words, a good risk prediction model should be able to exclude low-risk situations and clearly identify the high-risk situations that are related to some specific diseases. In the examination of health we are identifying different states of health without ground truth. So that by predicting risk of each participant by using classification approaches in the data mining. In this paper we proposed Mixed Probability Binary Rule Based Classification Algorithm is used to predict health risk of participate person. By implementing this algorithm we can get efficient classification result and also give better performance.

Keywords:

Data mining, Predict Analysis, Classification, Electronic Medical Records, Health Examination Records.

References:

[1] M. Woodward. Epidemiology: study design and data analysis. CRC Press, 2013. 
[2]. N. Esfandiary, M. R. Babavalian, A.-M. E. Moghadam, and V. K. Tabar. Knowledge discovery in medicine: Current issue and future trend. Expert Systems with Applications, 41(9), Jul. 2014. 
[3]. J.-Y. Yeh, T.-H. Wu, and C.-W. Tsao. Using data mining techniques to predict hospitalization of haemodialysis patients. Decision Support Systems, 50(2):439–448, Jan. 2011. 
[4]. F.Wang, P. Zhang, B. Qian, X.Wang, and I. Davidson. Clinical risk prediction with multilinear sparse logistic regression. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 145–154, New York, USA, 2014. ACM. 
[5]. T. Tran, D. Phung, W. Luo, and S. Venkatesh. Stabilized sparse ordinal regression for medical risk stratification. Knowledge and Information Systems, 43(3):555–582, mar 2015. 
[6]. J. Wiens, E. Horvitz, and J. V. Guttag. Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task. In Neural Information Processing Systems, pages 476–484, 2012. 
[7]. Y. Mao, W. Chen, Y. Chen, C. Lu, S. Louis, M. Kollef, and T. C. Bailey. An integrated data mining approach to real-time clinical monitoring and deterioration warning. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1140–1148, Beijing, China, 2012. ACM 
[8]. H. Neuvirth, M. Ozery-Flato, J. Hu, J. Laserson, M. S. Kohn, S. Ebadollahi, and M. Rosen-Zvi. Toward personalized care management of patients at risk: the diabetes case study. In SIGKDD, pages 395–403, California, USA, 2011. ACM. 
[9]T. Tran, D. Phung, W. Luo, and S. Venkatesh, “Stabilized sparse ordinal regression for medical risk stratification,” Knowledge and Information Systems, pp. 1–28, Mar. 2014.
[10] M. S. Mohktar, S. J. Redmond, N. C. Antoniades, P. D. Rochford, J. J. Pretto, J. Basilakis, N. H. Lovell, and C. F. McDonald, “Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data,” Artificial Intelligence in Medicine, vol. 63, no. 1, pp. 51–59, 2015.
[11] J. M. Wei, S. Q. Wang, and X. J. Yuan, “Ensemble rough hypercuboid approach for classifying cancers,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 3, pp. 381–391, 2010.
[12] E. Kontio, A. Airola, T. Pahikkala, H. Lundgren-Laine, K. Junttila, H. Korvenranta, T. Salakoski, and S. Salanter¨a, “Predicting patient acuity from electronic patient records.” Journal of Biomedical Informatics, vol. 51, pp. 8–13, 2014. 
[13].Q. Nguyen, H. Valizadegan, and M. Hauskrecht, “Learning classification models with soft-label information.” Journal of the American Medical Informatics Association : JAMIA, vol. 21, no. 3, pp. 501–8, 2014. 
[14] G. J. Simon, P. J. Caraballo, T. M. Therneau, S. S. Cha, M. R. Castro, and P. W. Li, “Extending Association Rule Summarization Techniques to Assess Risk of Diabetes Mellitus,” IEEE Transactions Knowledge and Data Engineering, vol. 27, no. 1, pp. 130–141, 2015. 
[15] L. Chen, X. Li, S. Wang, H.-Y. Hu, N. Huang, Q. Z. Sheng, and M. Sharaf, “Mining Personal Health Index from Annual Geriatric Medical Examinations,” in 2014 IEEE International Conference on Data Mining, 2014, pp. 761–766. 
[16] S. Pan, J. Wu, and X. Zhu, “CogBoost: Boosting for Fast Costsensitive Graph Classification,” IEEE Transactions on Knowledge and Data Engineering, vol. 6, no. 1, pp. 1–1, 2015.
[17]. Y. Zhao, G. Wang, X. Zhang, J. X. Yu, and Z. Wang, “Learning phenotype structure using sequence model,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 3, pp. 667–681, 2014. 
[18]. L. Chen, X. Li, Y. Yang, H. Kurniawati, Q. Z. Sheng, H.-Y. Hu, and N. Huang, “Personal health indexing based on medical examinations: A data mining approach,” Decision Support Systems, vol. 81, pp. 54 – 65, 2016.