The applications of big data in healthcare include, cost reduction in medical treatments, eliminate the risk factors associated with diseases, prediction of diseases, improves preventive care, analyzing drug efficiency. Some challenging tasks for the healthcare industry are:
1 (i) How to decide the most effective treatment for a particular disease?
2 (ii) How certain policies impact the outlay and behavior?
3 (iii) How does the healthcare cost likely to rise for different aspects of the future?
4 (iv) How the claimed fraudulently can be identified?
5 (v) Does the healthcare outlay vary geographically?
These challenges can be overcome by utilizing big data analytical tools and techniques. There are four major pillars of quality healthcare. Such as real-time patient monitoring, patient-centric care, improving the treatment methods, and predictive analytics of diseases. All these four pillars of quality healthcare can be potently managed by using descriptive, predictive, and prescriptive big data analytical techniques.
References
1. Smith, Rawat, P.S., Saroha, G.P., Bartwal, V., Evaluating SaaSModeler (small mega) Running on the Virtual Cloud Computing Environment using CloudSim. Int. J. Comput. Appl. (0975-8887), 5, 13, 2012c of September. London, A247, 529–551, April 1955.
2. Whitney, A. and Dwyer, II, S.J., the performance and implementation of the K-nearest neibbor decision rule with no training samples correctly identified, in: Proc. 4 Ann. Allerton Conf. On the Circuit System Band Theory, 1966.
3. Dasarathy, B.V., Nosing aroung the environment: A new structure and a system of classification rules for recognition in the most affected. IEEE Trans. Pattern Anal. Mach. Intell., PAMI- 2, 67–71, 1980.
4. Keller, J.M., Gray, M.R., Givens, Jr, J.A., A Fussy-K-Nearest Neighbor algorithm. IEEE Trans. Syst. Man Cybern., SMC-15, 4, 580–585, July/August 1985.
5. Bauer, E. and Kohavi, R., A comparison of voting classification algorithms impirical: Bagging, Improve and variants. Mach. Learn., 36.1, 36105–139, 1999.
6. Khan, M., Ding, Q., Perrizo, W., K-Nearest Neighbor Classification on Spatial Data Streams Using P-Trees1, 2. PAKDD 2002 LNAI, vol. 2336, pp. 517–528, 2002.
7. Catteddu, D. and Hogben, G., Cloud Computing: Benefits, risks and recommendations for information security, ENISA, Berlin, Heidelberg, 2009.
8. Phyu, T.N., Survey on Data Mining Classification Techniques. Proceedings of the International MultiConference of Engineers and Computer Scientists 2009, March 18-2009, vol. I, IMECS, 2009.
9. Ram, C.P. and Sreenivaasan, G., Security as a Service (SASS): Securing of user data by the coprocessor and distributing data. Trendz Information and Computing Sciences (TISC2010), December 2010, pp.152–155.
10. Yau, S.S. and Ho, G., Privacy protection in cloud computing sytems. Int. J. Software Inform., 4, 4, 351–365, December 2010.
11. Mishra, A., An Authentication Mechanism Based on Client-Server Architecture for Accessing Cloud Computing, International Journal of Emerging Technology Advanced Engineering, 2, 7, 95–99, July 2012.
12. Huang, S.-C. and Chen, B.-H., Highly accurate moving object detection in variable bit rate video-based traffic monitoring systems. IEEE Transactions on Neural Networks and Learning Systems, 24.12, 1920–1931, 2013.
13. Kafhali, S.E. and Haqiq, A., Effect of Mobility and Traffic Models on the energy consumption in MANET Routing Protocols. arXiv preprint arXiv:1304.3259, 2013.
14. Mishra, A. et al., A Review on DDOS Attack, TCP Flood Attack in Cloud Environment. Elsevier SSRN International Conference on Intelligent Communication and computation Research, Available at https://ssrn.com/abstract=3565043, March 31, 2020.
1 *Corresponding author: [email protected]
2 †Corresponding author: [email protected]
3 ‡Corresponding author: [email protected]
Конец ознакомительного фрагмента.
Текст предоставлен ООО «ЛитРес».
Прочитайте эту книгу целиком, купив полную легальную версию на ЛитРес.
Безопасно оплатить книгу можно банковской картой Visa, MasterCard, Maestro, со счета мобильного телефона, с платежного терминала, в салоне МТС или Связной, через PayPal, WebMoney, Яндекс.Деньги, QIWI Кошелек, бонусными картами или другим удобным Вам способом.
