Abstract
Due to technological advancement and digitalization within the medical field, there is tremendous growth in the medical data on a daily basis. The data includes but not limited to, personal data of patients and other frequent clinical data generated through health centers, government and private hospitals, etc. This data is represented in the form of electronic health records, registers, and patient wearable sensors and stored in a secure cloud. Eventually, the storage will have heterogeneous data. A large amount of information stored on cloud space is required to extract the data by applying the efficient big data analytic techniques, to assemble and handle the analysis. The various applications of big data analytics tools and techniques are growing quickly in the domain of management of diabetes mellitus treatment as it is considered one of the most common deficiencies of the current era. Applying machine learning and deep learning techniques to produce a prediction of disease risk and collect the various hospital’s performance related to diabetes. It provides a large number of benefits such as available treatments, costs of treatment, outbreaks prediction of epidemics, and to recommend the best health care system. In this chapter, we are going to discuss the various types of diabetes and its management, sustainable healthcare systems, and health care information exchanges using big data, decrease medical errors and supporting collaboration, etc. Also, the big data technology plays vital role to manage the diabetic mellitus treatment in the trustworthy and security. The identification of fraud in medical information and misuse of medical resources are discussed briefly in this chapter. A deep study is carried out on key issues and the actual use of big data analytics in diabetes healthcare.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Baynes, H. W. (2015). Classification, pathophysiology, diagnosis, and management of diabetes mellitus. Journal of Diabetes and Metabolism, 6(5). https://doi.org/10.4172/2155-6156.1000541.
Punthakee, Z., Goldenberg, R., & Katz, P. (2018). Definition, classification and diagnosis of diabetes, prediabetes and metabolic syndrome. Canadian Journal of Diabetes, 42, S10–S15.
American Diabetes Association. (2014). Diagnosis and classification of diabetes mellitus. Diabetes Care, 37, S81–S90.
American Diabetes Association. (2010, January). Diagnosis and classification of diabetes mellitus. Diabetes Care, 33(Suppl. 1), S62–S69.
Metzger, B. E., & Coustan, D. R. (Eds.). (1998). Proceedings of the Fourth International Workshop–Conference on Gestational Diabetes Mellitus. Diabetes Care, 21(Suppl. 2), B1–B167.
American Diabetes Association. (2003, January). Gestational diabetes mellitus. Diabetes Care, 26(Suppl. 1), s103–s105. https://doi.org/10.2337/diacare.26.2007.S103.
Udhayakumar, H. (2019). Safety measures for EHR systems. In Security and privacy of electronic healthcare records: Concepts, paradigms and solutions (pp. 272–289). London: Institution of Engineering and Technology. https://doi.org/10.1049/PBHE020E_ch10.
Wullianallur, R., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2014, 2–3. https://doi.org/10.1186/2047-2501-2-3.
Murdoch, T. B., & Detsky, A. S. (2016). The inevitable application of big data to health care. JAMA, 309, 1351–1352.
Zimmet, P., Alberti, K. G., & Shaw, J. (2001, December 13). Global and societal implications of the diabetes epidemic. Nature, 414(6865), 782–787.
Saravana Kumar, N. M., Eswari, T., Sampath, P., & Lavanya, S. (2015). Predictive methodology for diabetic data analysis in big data. Procedia Computer Science, 50, 203–208.
Ramesh, S., Caytiles, R. D., Iyengar, N. Ch. S. N. (2017). A deep learning approach to identify diabetes. UCI Repository of Bioinformatics Databases. Website: http://www.ics.uci.edu/~mlearn/MLRepository.html.
Anuja Kumari, V., & Chitra, R. (2013, March–April). Classification of diabetes disease using support vector machine. International Journal of Engineering Research and Applications (IJERA), 3(2), 1797–1801.
Raghupathi, W., & Raghupathi, V. (2014). Health Information Science and Systems, 2, 3. https://doi.org/10.1186/2047-2501-2-3.
El-Jerjawi, N. S., & Abu-Naser, S. (2018). Diabetes prediction using artificial neural network. Journal of Advanced Science, 124, 1–10.
Rouse, W. B., & Serban, N., (2014). Understanding and managing the complexity of healthcare. Cambridge, MA: The MIT Press.
Amit, B., Chinmay, C., Anand, K., & Debabrata, B. (2019). Emerging trends in IoT and big data analytics for biomedical and health care technologies. Handbook of data science approaches for biomedical engineering (Ch. 5, pp. 121–152). Amsterdam: Elsevier. ISBN: 9780128183182.
Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (The Morgan Kaufmann series in data management systems) (3rd ed.).
Alpaydin, E. (2004). Introduction to machine learning. Cambridge, MA: The MIT Press.
Swapna, G., Vinayakumar, R., & Soman, K. P. (2018). Diabetes detection using deep learning algorithms. ICT Express, 4, 243–246.
Akash, G., Chinmay, C., & Bharat, G. (2019). Sensing and monitoring of epileptical seizure under IoT platform. In Smart medical data sensing and IoT systems design in healthcare (pp. 201–223). IGI. https://doi.org/10.4018/978-1-7998-0261-7.ch009.
Chakraborty, C., Gupta, B., & Ghosh, S. K. (2013). A review on telemedicine-based WBAN framework for patient monitoring. International Journal of Telemedicine and e-Health, 19(8), 619–626. ISSN: 1530-5627.
Hardee, S. G., Osborne, K. C., Njuguna, N., Allis, D., Brewington, D., Patil, S. P., … Tanenberg, R. J. (2015). Interdisciplinary diabetes care: A new model for inpatient diabetes education. Diabetes Spectrum, 28(4), 276–282. https://doi.org/10.2337/diaspect.28.4.276.
Dhandhania, K. (2018). Towards data science. https://towardsdatascience.com/end-to-end-data-science-example-predicting-diabetes-with-logistic-regression-db9bc88b4d16.
Kadhm, M. S., Ghindawi, I. W., Mhawi, D. E. (2018). An accurate diabetes prediction system based on K-means clustering and proposed classification approach. International Journal of Applied Engineering Research. https://technostacks.com/blog/deep-learning-in-healthcare/.
Chakraborty, C. (2019). Mobile health (m-Health) for tele-wound monitoring. Mobile health applications for quality healthcare delivery (Ch. 5, pp. 98–116). IGI. https://doi.org/10.4018/978-1-5225-8021-8.ch005. ISBN: 9781522580218.
Appari, A., & Eric Johnson, M. (2010). Information security and privacy in healthcare: Current state of research. International Journal of Internet and Enterprise Management, 6(4), 279–314.
Ellaway, R. H., Pusic, M. V., Galbraith, R. M., & Cameron, T. (2014). Developing the role of big data and analytics in health professional education. Medical Teacher, 36(3), 216–222. https://doi.org/10.3109/0142159X.2014.874553.
Chu, T. (2016). Current challenges in the management of diabetes, meta cure Type 1 diabetes mellitus: Management challenges. US Pharmacist, 41(6), 21–26. Venkatachalam, M. (2019). Recurrent neural network: Towards data science. mayoclinic.org.
Wang, L., & Alexander, C. A. (2016). Big data analytics as applied to diabetes management. European Journal of Clinical and Biomedical Sciences, 2(5), 29–38. https://doi.org/10.11648/j.ejcbs.20160205.11.
Contreras, I., & Vehi, J. (2018). Artificial intelligence for diabetes management and decision support: Literature review. Journal of Medical Internet Research, 20, e10775. Introduction to BIG DATA: What is, types, characteristics & example. guru99.com.
Anderson, A. E., Kerr, W. T., Thames, A., Li, T., Xiao, J., & Cohen, M. S. (2016). Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected retrospective study. Journal of Biomedical Informatics, 54, 162–168. https://doi.org/10.1016/j.jbi.2015.12.006.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Rajesh Kumar, D., Rajkumar, K., Lalitha, K., Dhanakoti, V. (2021). Bigdata in the Management of Diabetes Mellitus Treatment. In: Chakraborty, C., Banerjee, A., Kolekar, M., Garg, L., Chakraborty, B. (eds) Internet of Things for Healthcare Technologies. Studies in Big Data, vol 73. Springer, Singapore. https://doi.org/10.1007/978-981-15-4112-4_14
Download citation
DOI: https://doi.org/10.1007/978-981-15-4112-4_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4111-7
Online ISBN: 978-981-15-4112-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)