System framework for cardiovascular disease prediction based on big data technology

Symmetry

Volumn 9, Issue 12, 2017, Pages

  Han, Sang Hun ^a   Kim, Kyoung Ok ^b   Cha, Eun Jong ^c   Kim, Kyung Ah ^c   Shon, Ho Sun ^c  

^a Chungbuk National University   (South Korea)

^b Woosong University   (South Korea)

^c Chungbuk National University College of Medicine   (South Korea)

Author keywords

Big data; Cardiovascular disease; Integrated database; Prediction system

Indexed keywords

EID: 85040036826     PISSN: None     EISSN: 20738994     Source Type: Journal    
DOI: 10.3390/sym9120293     Document Type: Article

References (31)

1. Myers, V., Broday, D.M., Steinberg, D.M., Drory, Y., Gerber, Y. Exposure to particulate air pollution and long-term incidence of frailty after myocardial infarction. Ann. Epidemiol. 2013, 23, 395-400. [CrossRef] [PubMed]
2. Lee, B.J., Kim, B., Lee, K. Air pollution exposure and cardiovascular disease. Toxicol. Res. 2014, 30, 71-75. [CrossRef] [PubMed]
3. Newby, D.E., Mannucci, P.M., Tell, G.S., Baccarelli, A.A., Brook, R.D., Donaldson, K., Forastiere, F., Franchini, M., Franco, O.H., Graham, I., et al. Expert position paper on air pollution and cardiovascular disease. Eur. Heart J. 2014, 36, 83-93. [CrossRef] [PubMed]
4. Goggins, W.B., Chan, E.Y., Yang, C.Y. Weather, pollution, and acute myocardial infarction in Hong Kong and Taiwan. Int. J. Cardiol. 2013, 168, 243-249. [CrossRef] [PubMed]
5. Lin, H., An, Q., Luo, C., Pun, V.C., Chan, C.S., Tian, L. Gaseous air pollution and acute myocardial infarction mortality in Hong Kong: A time-stratified case-crossover study. Atmos. Environ. 2013, 76, 66-73. [CrossRef]
6. Medina-Lezama, J., Morey-Vargas, O.L., Zea-Díaz, H., Bolaños-Salazar, J.F., Corrales-Medina, F., Cuba-Bustinza, C., Chirinos-Medina, D.A., Chirinos, J.A. Prevalence of lifestyle-related cardiovascular risk factors in Peru: The PREVENCION study. Rev. Panam. Salud Publ. Am. J. Public Health 2008, 24, 169-179. [CrossRef]
7. National Information Board. Personalized Health and Care 2020: Using Data and Technology to Transform Outcomes for Patients and Citizens; HM Government: London, UK, 2014.
8. Roski, J., Bo-Linn, G.W., Andrews, T.A. Creating value in health care through big data: Opportunities and policy implications. Health Aff. 2014, 33, 1115-1122. [CrossRef] [PubMed]
9. Salari, N., Shohaimi, S., Najafi, F., Nallappan, M., Karishnarajah, I. An improved artificial neural network based model for prediction of late onset heart failure. Life Sci. J. 2012, 9, 3684-3689.
10. Vijayashree, J., SrimanNarayanaIyengar, N.C. Heart disease prediction system using data mining and hybrid intelligent techniques: A review. Int. J. Bio-Sci. Biotechnol. 2016, 8, 139-148. [CrossRef]
11. Duan, L., Tang, C.J., Dong, G., Yang, N., Gou, C. Survey of emerging pattern based contrast mining and applications. J. Comput. Appl. 2012, 32, 304-308. [CrossRef]
12. Abbes, H., Gargouri, F. Big data integration: A MongoDB database and modular ontologies based approach. Procedia Comput. Sci. 2016, 96, 446-455. [CrossRef]
13. Dean, J., Ghemawat, S. MapReduce: Simplified data processing on large clusters. Commun. ACM 2008, 51, 107-113. [CrossRef]
14. Eckelman, M.J., Sherman, J. Environmental impacts of the U.S. health care system and effects on public health. PLoS ONE 2016, 11, 1-14. [CrossRef] [PubMed]
15. Ministry of Environment. Health Impact Assessment According to Climate Change Linked with Big Data of National Health Insurance; Korea Environment Institute: Chungnam, Korea, 2015.
16. Tekiner, F., Keane, J.A. Big data framework. In Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK, 13-16 October 2013; pp. 1494-1499.
17. Lambda Architecture. Available online: http://lambda-architecture.net (accessed on 25 November 2017).
18. Kiran, M., Murphy, P., Monga, I., Dugan, J., Baveja, S.S. Lambda architecture for cost-effective batch and speed big data processing. In Proceedings of the 2015 IEEE International Conference on Big Data, Santa Clara, CA, USA, 29 October-1 November 2015; pp. 2785-2792.
19. Amazon Web Services. Lambda Architecture for Batch and RealTime Processing on AWS with Spark Streaming and Spark SQL; AmazonWeb Services Inc.: Seattle, WA, USA, 2015.
20. Zikopoulos, P., Eaton, C. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data; McGraw-Hill Osborne Media: Berkeley, CA, USA, 2011.
21. Familiar, B., Barnes, J. Real-time processing using azure stream analytics. In Business in Real-Time Using Azure IoT and Cortana Intelligence Suite; Familiar, B., Barnes, J., Eds., Apress: New York, NY, USA, 2017; pp. 169-226.
22. Marcu, O.C., Tudoran, R., Nicolae, B., Costan, A., Antoniu, G., Pérez-Hernández, M.S. Exploring shared state in key-value store for window-based multi-pattern streaming analytics. In Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Madrid, Spain, 14-17 May 2017; pp. 1044-1052.
23. Sideris, K., Nejabati, R., Simeonidou, D. Seer: Empowering software defined networking with data analytics. In Proceedings of the International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security, Granada, Spain, 14-16 December 2016; pp. 181-188.
24. Karim, M.R., Kaysar, M.M. Large Scale Machine Learning with Spark; Packt Publishing: Birmingham, UK, 2016.
25. Li, B., Song, M., Ou, Z., Haihong, E. Performance comparison and analysis of yarn's schedulers with stress cases. In Proceedings of the 7th International Conference on Cloud Computing and Big Data, Macau, China, 16-18 November 2016; pp. 93-98.
26. Dolev, S., Florissi, P., Gudes, E., Sharma, S., Singer, I. A survey on geographically distributed big-data processing using MapReduce. IEEE Trans. Big Data 2017. [CrossRef]
27. Rodrigues, R.A., Lima, L.A., Goncalves, S.G., Mialaret, F.S., Da Chnha, A.M., Vieira, L.A. Integrating NoSQL, Relational Database, and the Hadoop Ecosystem in an Interdisciplinary Project involving Big Data and Credit Card Transactions. In Information Technology-New Generations, 14th International Conference on Information Technology, Las Vegas, NV, USA, 10-12 April; Springer: New York, NY, USA, 2017; pp. 443-451.
28. Bagwari, N., Kumar, O. Indexing optimizations on Hadoop. In Proceedings of the 3rd International Conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad, India, 9-10 February 2017; pp. 1-7.
29. Kumar, R., Parasher, B.B., Gupta, S., Sharma, Y., Gupta, N. Apache Hadoop, NoSQL and NewSQL solutions of big data. Int. J. Adv. Found. Res. Sci. Eng. 2014, 1, 28-36.
30. Gribaudo, M., Iacono, M., Kiran, M. A performance modeling framework for lambda architecture based applications. Future Gener. Comput. Syst. 2017. [CrossRef]
31. Decaneto, A. Design and testing of an active big data architecture for social and crowding emergencymanagement. PolitecnicoMilano 2017. Available online: http://hdl.handle.net/10589/134427 (accessed on 25 November 2017).