A Survey on Big Data in Healthcare Applications

The large volume of healthcare data continues to mount every second, making it harder and very difficult to find any form of useful information. Recently, big data is changing the traditional way of the data delivery system into valuable insights, especially in the healthcare industry. It provides a lot of benefits in the healthcare sector to detect critical diseases at the initial stage and deliver better healthcare services to the right patient at the right time. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured vital data rapidly produced by the various healthcare information storage systems. However, there are several issues to be addressed in the current health data analytics platforms that offer technical methods such as vital data collection, aggregation, process, analysis, visualization, and sharing. Due to lack of detailed analysis in the existing research works, this paper examines the most significant characteristics of big data analytics in health care, various data sources and its data types, five R’s of big data analytics, and then briefly discusses the recent open research challenges with future directions.

[1]  Mamta Mittal,et al.  Big Data and Machine Learning Based Secure Healthcare Framework , 2018 .

[2]  Ming Chen,et al.  Big Data Analytics in Medicine and Healthcare , 2018, J. Integr. Bioinform..

[3]  Abhishek Narain Singh,et al.  Customized biomedical informatics , 2018 .

[4]  Taghi M. Khoshgoftaar,et al.  Big Data fraud detection using multiple medicare data sources , 2018, J. Big Data.

[5]  D. P. Acharjya,et al.  A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools , 2016 .

[6]  Siddharth Swarup Rautaray,et al.  Big Data Analytics for Medical Applications , 2018 .

[7]  Muhammad Shiraz,et al.  Big Data: Survey, Technologies, Opportunities, and Challenges , 2014, TheScientificWorldJournal.

[8]  Yongzhao Zhan,et al.  Maximum Neighborhood Margin Discriminant Projection for Classification , 2014, TheScientificWorldJournal.

[9]  Raymond Y. K. Lau,et al.  Smart health: Big data enabled health paradigm within smart cities , 2017, Expert Syst. Appl..

[10]  Fuad Rahman,et al.  Application of big-data in healthcare analytics — Prospects and challenges , 2016, 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).

[11]  Yichuan Wang,et al.  An integrated big data analytics-enabled transformation model: Application to health care , 2018, Inf. Manag..

[12]  Luxia Zhang,et al.  Big data and medical research in China , 2018, British Medical Journal.

[13]  P. Bonato,et al.  From A to Z: Wearable technology explained. , 2018, Maturitas.

[14]  Amir Hossein Ghapanchi,et al.  Analysis of Research in Healthcare Data Analytics , 2016, ArXiv.

[15]  Laila Benhlima,et al.  Big Data Management for Healthcare Systems: Architecture, Requirements, and Implementation , 2018, Adv. Bioinformatics.

[16]  D. Siva Sankara Reddy,et al.  Big Data Analytics for Healthcare Organization: A Study of BDA Process, Benefits and Challenges of BDA , 2017 .

[17]  Abderrahim Beni Hssane,et al.  Big healthcare data: preserving security and privacy , 2018, Journal of Big Data.

[18]  Hajar Mousannif,et al.  Big data in healthcare: Challenges and opportunities , 2015, 2015 International Conference on Cloud Technologies and Applications (CloudTech).

[19]  A. P. Siva Kumar,et al.  Privacy preservation techniques in big data analytics: a survey , 2018, Journal of Big Data.

[20]  Viju Raghupathi,et al.  Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.