Open Access Open Access  Restricted Access Subscription Access

Big Data Analytics in Health Care: A Review Paper


Affiliations
1 Department of Computer Science, Al-Albayt University, Jordan
 

The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.

Keywords

Big Data, Healthcare, Big Data Challenges, EHRs.
User
Notifications
Font Size

  • S. Singh, T. Firdaus, and A. K. Sharma, (2015) "Survey on Big Data Using Data Mining", International Journal of Engineering Development and Research, Vol. 3, No.4, pp. 135–143.
  • S. Dash, S. K. Shakyawar, M. Sharma, and S. Kanshik, (2019) "Big data in healthcare: management, analysis and future prospects", Journal of Big Data. Springer International Publishing, Vol. 6, No.1.
  • I. K. Subagja, N. Amaliyah, U. Hiermy, B. T. Rahardjo, E. L. Lydia, K. Shakar, and P. T. Naguyen, (2019) "Evaluation of big data analytics in medical science", International Journal of Engineering and Advanced Technology, Vol. 8, (6 Special Issue 3), pp. 717–720.
  • N. Zulkarnain, M. Anshari, and A. Definition, (2016) "Big Data : Concept, Applications, & Challenges", International Conference on Information Management and Technology, pp. 307–31.
  • S. H. Kaisler, F. J. Armour, and A. J. Espinosa, "Introduction to the big data and analytics: concepts, (2016) techniques, methods, and applications minitrack", Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 1059–1060.
  • J. Amudhavel, V. Padmapriya, V. Gowri, K. Lakshmipriya, K. P. Kumar, and B. Thiyagarajan, (2015) "Perspectives, motivations and implications of big data analytics.", In Proceedings of the 2015 International Conference on Advanced Research in Computer Science Engineering & Technology, pp. 1-5.
  • M. Pospiech, and C. Felden, (2012) "Big data - A State-of-the-Art", 18th Americas Conference on Information Systems 2012, AMCIS 2012, Vol. 5, pp. 3918–3928.
  • S. Brown, (2020) "Data Characteristics", A multidisciplinary journal of global macro trends BIG, Vol.3, No.6, pp. 19–27.
  • M. I. Razzak, M. Imran, and G. Xu, (2020) "Big data analytics for preventive medicine", Neural Computing and Applications. Springer London. Vol. 32, No.9, pp.4417-4451.
  • G. Chen, and M. Islam, (2019) "Big Data Analytics in Healthcare", Proceedings - 2019 2nd International Conference on Safety Produce Informatization, IICSPI 2019, Vol. 2015, pp. 227–230.
  • S. G. Alonso, I. Torre-Diez, J. Rodigues, S. Hamorioui, and M. L. Coronado, (2017) "A Systematic Review of Techniques and Sources of Big Data in the Healthcare Sector.", Journal of Medical Systems, Vol. 41, No. 11, pp.183.
  • A. P. Ambinder, (2005) "Electronic Health Records By", Journal of Oncology Practice, Vol. 1, No. 2, pp. 57–63.
  • R. S. Evans, (2016) "Electronic Health Records: Then, Now, and in the Future", Yearbook of medical informatics, pp. S48–S61.
  • Z. Liang et al., (2014) "Deep learning for healthcare decision making with EMRs", Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014, (Cm), pp. 556–559.
  • M. Calvert et al., (2015) "Putting patient-reported outcomes on the Big Data Road Map", Journal of the Royal Society of Medicine, Vol.108, No.8, pp. 299–303.
  • M. Uddin, and S. Syed‐abdul, (2020) "Data analytics and applications of the wearable sensors in healthcare: An overview", Sensors (Switzerland), Vol.20, No.5.
  • J. Zhang, N. Xue, and X. Huang, (2016) "A Secure System for Pervasive Social Network-Based Healthcare", IEEE Access, Vol.4, pp. 9239–9250.
  • A. Kankanhalli et al., (2016) "Big data and analytics in healthcare: Introduction to the special section", Information Systems Frontiers, Vol.18, No.2, pp. 233–235.
  • B. Van Calster et al., (2019) "Predictive analytics in health care: how can we know it works?" Journal of the American Medical Informatics Association, Vol.26, No.12, pp. 1651–1654.
  • A. Asante-Korang, and J. P. Jacobs, (2016) "Big Data and paediatric cardiovascular disease in the era of transparency in healthcare", Cardiology in the Young, Vol.26, No.8, pp. 1597–1602.
  • A. ISMAIL, S. ABDLERAZEK, I. M. EL-HENAWY, (2020) "Big Data Analytics in Heart Diseases", Journal of Theoretical and Applied Information Technology, Vol. 98, No.11, pp. 1970–1980.
  • N. Jothi., N. A. Rashid, and W. Husain, (2015) "Data Mining in Healthcare - A Review", Procedia Computer Science. Elsevier Masson SAS, Vol.72, pp. 306–313.
  • M. U. Nisar, A. Fard, and J. A. Miller, (2013) "Techniques for graph analytics on big data", Proceedings - 2013 IEEE International Congress on Big Data, BigData 2013, pp. 255–262.
  • V. N. Gudivada, D. Rao, and V. V. Raghavan, (2015) "Big Data Driven Natural Language Processing Research and Applications", Handbook of Statistics. Elsevier Inc, vol.33, pp.203-238.
  • Y. Wang et al., (2014) "Energy efficient neural networks for big data analytics", Proceedings -Design, Automation and Test in Europe, DATE, pp. 2–3.
  • J. Qiu et al., (2016) "A survey of machine learning for big data processing", Eurasip Journal on Advances in Signal Processing. EURASIP Journal on Advances in Signal Processing, Vol.1.
  • M. R. Ghazi, and D. Gangodkar, (2015) "Hadoop, mapreduce and HDFS: A developers perspective", Procedia Computer Science. Elsevier Masson SAS, Vol.48, pp. 45–50.
  • A. B. Patel, M. Birla, and U. Nair, (2012) "Addressing big data problem using Hadoop and Map Reduce", 3rd Nirma University International Conference on Engineering, NUiCONE 2012, pp. 6–8.
  • A. Alam, and J. Ahmed, (2014) "Hadoop architecture and its issues", Proceedings - 2014 International Conference on Computational Science and Computational Intelligence, CSCI 2014, Vol.2, pp. 288–291.
  • K. Dwivedi, and S. K. Dubey, (2014) "Analytical review on Hadoop Distributed file system", Proceedings of the 5th International Conference on Confluence 2014: The Next Generation Information Technology Summit, pp. 174–181.
  • I. A. T. Hashem et al., (2020) "MapReduce scheduling algorithms: a review", Journal of Supercomputing, Vol.76, No.7, pp.4915-4945.
  • A. L’heureux, K. Grolinger, J. F. Elyamany, and M. A. Capretz, (2017) "Machine learning with big data: Challenges and approaches", IEEE Access, Vol. 5, pp. 7776-7797.
  • A. Ghazvini, and Z. Shukur, (2013) "Security Challenges and Success Factors of Electronic Healthcare System", Procedia Technology. Elsevier B.V., Vol.11, pp. 212–219.
  • M. Padgavankar, and S. Gupta, (2014) "Big data storage and challenges", International Journal of Computer Science and Information Technologies, Vol.5, No.2, pp. 2218–2223.
  • J. Li et al., (2014) "The overview of big data storage and management", Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014, pp. 510–513.
  • P. Kostkova, "Who Owns the Data ?", Open Data for Healthcare, vol.4.
  • R. Nambiar et al., (2013) "A look at challenges and opportunities of Big Data analytics in healthcare", Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013, pp. 17–22.
  • A. Gardiner et al., (2018) "Skill Requirements in Big Data: A Content Analysis of Job Advertisements", Journal of Computer Information Systems. Taylor & Francis, Vol.58, No.4, pp. 374–384.

Abstract Views: 323

PDF Views: 146




  • Big Data Analytics in Health Care: A Review Paper

Abstract Views: 323  |  PDF Views: 146

Authors

Maria Mohammad Yousef
Department of Computer Science, Al-Albayt University, Jordan

Abstract


The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.

Keywords


Big Data, Healthcare, Big Data Challenges, EHRs.

References