Document Type : Articles

Author

KolaDaisi University

Abstract

The advent of new technologies such as Machine Learning has highly influenced the health sector's activities; with this, there is an ease in diagnosis and decision-making processes in the sector. Hence, this study aims to analyze the application of Machine Learning in Smart Health research. This study uses 192 records from the Scopus database based on a well-crafted search term to identify nations with the highest publication output, the principal research subject areas, the top funding sponsors, and research keywords in this subject matter. The result shows that the first document on machine learning in smart health was published in 2011. The research output on this subject has dramatically increased, with India now being the top nation where research in this area is conducted. It was also discovered that the journal IEEE Access has the highest number of publications in this area. This analysis will help researchers, policy developers, and professionals in the health sector to better understand the development of Machine Learning in Smart Health research. Machine Learning in Smart Health portends Growth in the future.

Keywords

Main Subjects

Archambault, E., Campbell, D., Gingras Y. & Lariviere, V. (2009). Comparing bibliometric statistics obtained from the Web of Science and Scopus. Journal of the American Society for Information Science and Technology, 60(7), 1320–1326. https://doi.org/10.1002/asi.21062
Bajwa, R. S. & Yaldram, K. (2012). Research output in nanoscience and nanotechnology: Pakistan scenario. Journal of Nanoparticle Research, 14(2), 1–6.
Das, B., Chen, C., Seelye, A. M. & Cook, D. J. (2011, June). An automated prompting system for smart environments. In International Conference on Smart Homes and Health Telematics (pp. 9-16). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21535-3_2
Elsevier (2016). Scopus. Retrieved from https://www.elsevier.com/solutions/scopus
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M. & Chou, K. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29.
Grewal, A., Kayr, M. & Park, J.H. (2019). A unified framework for behavior monitoring and abnormality detection for smart home. Wireless Communications and Mobile Computing, 2019. https://doi.org/10.1155/2019/1734615
Guo, Y., Hao, Z., Zhao, S., Gong, J. & Yang, F. (2020). Artificial Intelligence in Health Care: Bibliometric Analysis. Journal of Medical Internet Research, 22(7), e18228. https://doi.org/10.2196/18228
Hashimoto, D.A., Rosman, G., Rus, D. & Meireles, O.R. (2018). Artificial Intelligence in Surgery: Promises and Perils. Annals of Surgery, 268(1), 70-76.
Houssami, N., Kirkpatrick-Jones, G., Noguchi, N. & Lee, C. I. (2019). Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI's potential in breast screening practice. Expert review of medical devices16(5), 351-362. https://doi.org/10.1080/17434440.2019.1610387.
Joloudari, J. H., Saadatfar, H., Dehzangi, A. & Shamshirband, S. (2017). Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection. Informatics in Medicine Unlocked, 17, 100255.
Kang, M., Park, E., Cho, B.H. & Lee, K.S. (2018). Recent Patient health monitoring platforms incorporating internet of things-enabled smart devices. International Neurology Journal, 22(4), 313
Khedkar, V.S. & Patel, S. (2021). Diabetes prediction using machine learning: A bibliometric analysis, Library Philosophy and Practice, 4751.
Li, Y., Shan, B., Li, B., Liu, X. & Pu, Y. (2021). Literature review on the applications of machine learning and blockchain technology in smart healthcare industry: a bibliometric analysis. Journal of Healthcare Engineering, 2021.https://doi.org/10.1155/2021/9739219
Mahapatra, M. (1985, July). On the validity of the theory of exponential growth of scientific literature. In Proceedings of the 15th IASLIC conference, Bangalore (Vol. 6170).
Müller, A. M., Alley, S., Schoeppe, S. & Vandelanotte, C. (2016). The effectiveness of e-& mHealth interventions to promote physical activity and healthy diets in developing countries: a systematic review. International Journal of Behavioral Nutrition and Physical Activity, 13(1).
Niu, B.  Hong, S.  Yaun, J. Peng, S. Wang, Z.  & Zhang, X. (2014). Global trends in sediment-related research in earth science during 1992-2011: a bibliometric analysis. Scientometrics, 98(2), 511-529. https://doi.org/10.1007/s11192-013-1065-x.
Oladimeji, O. O., Oladimeji, A. & Oladimeji, O. (2021). Classification models for likelihood prediction of diabetes at early stage using feature selection. Applied Computing and Informatics. https://doi.org/10.1108/ACI-01-2021-0022
Reddy, S. (2018). Use of Artificial Intelligence in Healthcare Delivery. EHealth-Making Health Care Smarter. https://doi.org/10.5772/intechopoen.74714.
Saifi, S., Taylor, A.J., Allen, J. & Hendel, R. (2013). The use of a learning community and online evaluation of utilization for SPECT mycordial perfusion imaging, JACC Cardiovasc Imaging, 6(7), 823-829.
Santo, B.S., Steiner, M.T.A., Fenerich, A.T. & Lima, R.H.P. (2019). Data mining and machine learning techniques applied to public health problems: A bibliometric analysis from 2009 to 2018. Computers & Industrial Engineering, 138. https://doi.org/10.1016/j.cie.2019.106120
Singh, V. & Gochhait, S. (2020). The development of artificial intelligence in health and medicine: A bibliometric analysis, European Journal of Molecular & Clinical Medicine, 7(6), 2585-2594.
Tran, B. X., McIntyre, R. S., Latkin, C. A., Phan, H. T., Vu, G. T., Nguyen, H. L. T., Gwee, K. K., Ho, C. S. H. & Ho, R. C. M. (2019). The Current Research Landscape on Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis. Int J Environ Res Public Health, 16(12). https://doi.org/10.3390/ijerph16122150.
Fosso Wamba, S. & Queiroz, M. M. (2021). Responsible artificial intelligence as a secret ingredient for digital health: Bibliometric analysis, insights, and research directions. Information Systems Frontiers.https://doi.org/10.1007/s10796-021-10142-8
Yu, K.H., Beam, L.A. & Kohane, I.S. (2018) Artificial Intelligence in Healthcare. Nature Biomedical Engineering,2, 719-731.