Table of Contents Author Guidelines Submit a Manuscript
Scientific Programming
Volume 2017 (2017), Article ID 1901876, 18 pages
https://doi.org/10.1155/2017/1901876
Research Article

Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm

School of Science, Edith Cowan University, Joondalup, WA, Australia

Correspondence should be addressed to Hongye Zhong; ua.ude.uce.ruo@gnohzh

Received 5 January 2017; Revised 14 April 2017; Accepted 9 May 2017; Published 28 June 2017

Academic Editor: Michele Risi

Copyright © 2017 Hongye Zhong and Jitian Xiao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Wang, Z.-Q. Zhao, X. Hu, Y.-M. Cheung, H. Hu, and F. Gu, “Online learning towards big data analysis in health informatics,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8211, pp. 516–523, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. W. Liu and E. Park, “Big data as an e-health service,” in Proceedings of the 2014 International Conference on Computing, Networking and Communications ICNC 2014, pp. 982–988, February 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. E. Mezghani, E. Exposito, K. Drira, M. Da Silveira, and C. Pruski, “A Semantic Big Data Platform for Integrating Heterogeneous Wearable Data in Healthcare,” Journal of Medical Systems, vol. 39, no. 12, article no. 185, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Deng and D. Yu, “Deep learning: Methods and applications,” Foundations and Trends in Signal Processing, vol. 7, no. 3-4, pp. 197–387, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. H. Chai and B. Wang, “A hierarchical situation assessment model based on fuzzy Bayesian network,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7003, no. 2, pp. 444–454, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Wang, L. Kung, and T. A. Byrd, “Big data analytics: understanding its capabilities and potential benefits for healthcare organizations,” Technological Forecasting & Social Change, pp. 1–11, 2016. View at Publisher · View at Google Scholar
  7. N. Jothi, N. A. Rashid, and W. Husain, “Data Mining in Healthcare - A Review,” in Proceedings of the 3rd Information Systems International Conference, pp. 306–313, April 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Theodoridis, Machine Learning - A Bayesian and Optimation Perspective, Elsevier, 2015.
  9. S. Zillner and S. Neururer, “Big data in the health sector,” in New Horizons for a Data-Driven Economy, pp. 179–194, Springer International Publishing.
  10. I. Goodfellow and A. Courville, Deep learning, MIT Press, 2016.
  11. Z. Liang, G. Zhang, J. X. Huang, and Q. V. Hu, “Deep learning for healthcare decision making with EMRs,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, pp. 556–559, November 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. E. Bosse, J. Roy, and S. Wark, Concepts, Models, and Tools for Information Fusion, Artech House, 2007.
  13. X. Gao, W. Lia, M. Loomesa, and L. Wang, “A fused deep learning architecture for viewpoint classification of echocardiography,” Information Fusion, vol. 36, pp. 103–113, 2017. View at Google Scholar
  14. NIST Big Data Public Working Group, “NIST Big Data Interoperability Framework,” 2015, http://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1500-4.pdf.
  15. Y. Wang, L. Kung, C. Ting, and T. A. Byrd, “Beyond a technical perspective: Understanding big data capabilities in health care,” in Proceedings of the 48th Hawaii International Conference on System Sciences, pp. 3044–3053, January 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. B. Lantz, Machine Learning with R 2ed, Packt Publishing, 2015.
  17. J. Bell, Machine Learning - Hands-On for Developers and Technical Professionals, John Wiley & Sons, 2015.
  18. E. Vermeulen-Smit, M. Ten Have, M. Van Laar, and R. De Graaf, “Clustering of health risk behaviours and the relationship with mental disorders,” Journal of Affective Disorders, vol. 171, pp. 111–119, 2015. View at Publisher · View at Google Scholar · View at Scopus
  19. D. Talia, D. Talia, and F. Marozzo, Data Analysis in the Cloud - Models, Techniques and Applications, Elsevier Inc, 2016.
  20. J. Kazmierska and J. Malicki, “Application of the Naïve Bayesian Classifier to optimize treatment decisions,” Radiotherapy and Oncology, vol. 86, no. 2, pp. 211–216, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. P. Harrington, “Machine learning in action,” Mainning Publications, 2012. View at Google Scholar
  22. J. Sander and J. Beyerer, “Bayesian fusion: Modeling and application,” in Proceedings of the Sensor Data Fusion: Trends, Solutions, Applications, pp. 1–6, October 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. L. A. Klein, Sensor and Data Fusion: A Tool for Information Assessment and Decision Making, SPIE Press, 2004.
  24. A. Gelman, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin, Bayesian Data Analysis, CRC Press, 3rd edition, 2014.
  25. N. Fenton and M. Neil, “Decision support software for probabilistic risk assessment using bayesian networks,” IEEE Software, vol. 31, no. 2, pp. 21–26, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. M. L. Vaneek, “Introduction into Bayesian Networks,” 2016, http://www.fit.vutbr.cz/study/courses/VPD/public/0809VPD-Vanek.pdf.
  27. D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012.
  28. D. E. Holmes and L. C. Jain, Innovations in Bayesian Networks, Springer, 2008. View at Publisher · View at Google Scholar
  29. A. Esposito, S. Bassis, and F. C. Morabito, “Recent advances of neural networks models and applications: An introduction,” Smart Innovation, Systems and Technologies, vol. 37, pp. 3–8, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Samarasinghe, Neural Network for Applied Sciences and Engineering, Auerbach Publications, 2007.
  31. M. Kirk, Thoughtful Machine Learning, O’Reilly Media.
  32. N. Lewis, Deep Learning Made Easy with R, CreateSpace, 2016.
  33. N. Dhungela, G. Carneirob, and A. P. Bradley, “A deep learning approach for the analysis of masses in mammograms with minimal user intervention,” Medical Image Analysis, vol. 37, pp. 114–128, 2017. View at Google Scholar
  34. M. Pereza, S. Avilab, D. Moreiraa et al. et al., “Video pornography detection through deep learning techniques and motion information,” Neurocomputing, vol. 230, pp. 279–293, 2017. View at Google Scholar
  35. Q. Zhang, L. T. Yang, and Z. Chen, “Privacy preserving deep computation model on cloud for big data feature learning,” Institute of Electrical and Electronics Engineers. Transactions on Computers, vol. 65, no. 5, pp. 1351–1362, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  36. S. Das, High-Level Data Fusion, Artech House, 2008.
  37. M. K. Sundareshan and Y. C. Wong, “Nueral network-based fusion of image and non-image data for surveillance and tracking application,” Data Fusion for Situation Monitoring, Incident Detection, Alert and Response Management, no. IOS Press, 2015.
  38. H. B. Mitchell, “Data fusion: Concepts and ideas,” Data Fusion: Concepts and Ideas, pp. 1–344, 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. D. L. Hall and J. Llinas, Handbook of Multisensor Data Fusion, CRC Press, 2001.
  40. E. Bosse and B. Solaiman, Information Fusion and Analytics for Big Data and IoT, Artech House, 2016.
  41. L. Wald, Data Fusion: Definitions and Architectures, Les Presses de L'Ecole des Mines, 2002.
  42. M. C. Amirani, M. Toorani, and A. A. Beheshti, “A new approach to content-based file type detection,” in Proceedings of the 13th IEEE Symposium on Computers and Communications, ISCC 2008, pp. 1103–1108, July 2008. View at Publisher · View at Google Scholar · View at Scopus
  43. D. Tian, A. Gledson, A. Antoniades, A. Aristodimou, and N. Dimitrios, “A bayesian association rule mining algorithm,” in Proceedings of the IEEE International Conference on Systems, pp. 3258–3264, October 2013. View at Publisher · View at Google Scholar · View at Scopus
  44. D. Allard, A. Comunian, and P. Renard, “Probability aggregation methods in geoscience,” Mathematical Geosciences, vol. 44, no. 5, pp. 545–581, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  45. O. Abdel-Hamid, A.-R. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, “Convolutional neural networks for speech recognition,” IEEE Transactions on Audio, Speech and Language Processing, vol. 22, no. 10, pp. 1533–1545, 2014. View at Publisher · View at Google Scholar · View at Scopus
  46. M. Sun, Z. Song, X. Jiang, J. Pan, and Y. Pang, “Learning Pooling for Convolutional Neural Network,” Neurocomputing, vol. 224, pp. 96–104, 2017. View at Google Scholar
  47. Q. Li, Z. Jin, C. Wang, and D. D. Zeng, “Mining opinion summarizations using convolutional neural networks in Chinese microblogging systems,” Knowledge-Based Systems, vol. 107, pp. 289–300, 2016. View at Publisher · View at Google Scholar · View at Scopus
  48. R. Prado and M. West, Time Series - Modeling, Computation, and Inference, CRC Press, 2010.
  49. D. Miner and A. Shook, MapReduce Design Patterns, O’Reilly Media, 2013.
  50. B. Holt, Writing and Querying MapReduce Views in CouchDB, O’Reilly Media, 2011.
  51. V. Prajapati, Big Data Analytics with R and Hadoop, Packt Publishing, 2013.