Abstract
The information and communication technology (ICT)-related exponential growth has increased the demand for big data analytics (BDA). BDA involves the handling of a gigantic data for storage and investigation. The evolving field of BDA owns many challenges in various fields including drug delivery, healthcare, surveillance, weather forecasting, etc. In comparison with other industries, the need for big data in healthcare experiences more attention in present days. Initially, the data collected from remote healthcare services vary based on value, variety, velocity, veracity, and volume since the collection occurs at different locations using various devices. In research and development, there is an urge for an algorithm in risk prediction of heart attack. One of the major diseases related to mortality is cardiovascular disease (CVD). Further, an approach is introduced, and this approach has improved performance in terms of accuracy of 99%. However, in future works, it is recommended to focus on various other nature-inspired algorithms for diseases such as thyroid, diabetes, and so on.
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References
Wyber R, Vaillancourt S, Perry W, Mannava P, Folaranmi T, Celi LA (2015) Big data in global health: improving health in low- and middle-income countries. B World Health Organ 93(3): 203–208. https://doi.org/10.2471/BLT.14.139022
Dhanalakshmi P, Ramani K, Eswara Reddy B (2017) An improved rank based disease prediction using web navigation patterns on bio-medical databases. Future Comput Inform J 2(2):133–147. https://doi.org/10.1016/j.fcij.2017.10.003
Derhami S, Smith AE (2017) An integer programming approach for fuzzy rule-based classification systems. Eur J Oper Res 256(3):924–934 [Online]. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0377221716305240
Gupta N, Ahuja N, Malhotra S, Bala A, Kaur G (2017) Intelligent heart disease prediction in cloud environment through ensembling. Expert Syst 34(3):e12207 [Online]. Available from: http://doi.wiley.com/10.1111/exsy.12207
Suinesiaputra A, Medrano-Gracia P, Cowan BR, Young AA (2015) Big heart data: advancing health informatics through data sharing in cardiovascular imaging. IEEE J Biomed Health Inf 19(4):1283–1290 [Online]. Available from: http://ieeexplore.ieee.org/document/6957068/
Chen M, Hao Y, Hwang K, Wang L, Wang L (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5:8869–8879 [Online]. Available from: http://ieeexplore.ieee.org/document/7912315/
Chen Y-C, Pal NR, Chung I-F (2012) An integrated mechanism for feature selection and fuzzy rule extraction for classification. IEEE Trans Fuzzy Syst 20(4):683–698 [Online]. Available from: http://ieeexplore.ieee.org/document/6112676
Heinrich A, Lojo A, González AR, Vasiljevs A, Garattini C, Costa-Soria C, Hamelinck D, Artigot EN, Menasalvas E, Xu HF, Sasaki F, Aarestrup FM, Kerremans GR, Thoms J, Sanchez MM (2016) Big data technologies in healthcare: needs, opportunities and challenges. TF7 Healthcare subgroup [Online]. Available from: http://www.bdva.eu/sites/default/files/Big%20Data%20Technologies%20in%20Healthcare.pdf. Accessed: 14 Feb 2018
Jindal A, Dua A, Kumar N, Vasilakos AV, Rodrigues JJPC (2017) An efficient fuzzy rule-based big data analytics scheme for providing healthcare-as-a-service. In: 2017 IEEE international conference on communications (ICC), May 2017, IEEE, pp 1–6 [Online]. Available from: http://ieeexplore.ieee.org/document/7996965/
Stylianou A, Talias MA (2017) Big data in healthcare: a discussion on the big challenges. Health Technol 7(1):97–107 [Online]. Available from: http://link.springer.com/10.1007/s12553-016-0152-4
Taylor RA, Pare JR, Venkatesh AK, Mowafi H, Melnick ER, Fleischman W, Hall MK (2016) Prediction of in-hospital mortality in emergency department patients with sepsis: a local big data-driven, machine learning approach. Acad Emerg Med Official J Soc Acad Emerg Med 23(3):269–278 [Online]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26679719
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Jain, A., Bhatnagar, V., Rao, A.C.S. (2021). Smart Heart Attack Forewarning Model Using MapReduce Programming Paradigm. In: Goar, V., Kuri, M., Kumar, R., Senjyu, T. (eds) Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, vol 135. Springer, Singapore. https://doi.org/10.1007/978-981-15-5421-6_5
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DOI: https://doi.org/10.1007/978-981-15-5421-6_5
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