Abstract
Smart medicinal services is an inventive procedure of synergizing the advantages of sensors, Internet of things (IoT), and large information Analytics to convey improved patient consideration while lessening the human services costs. The Medical Services industry faces tremendous difficulties to spare the information produced and to process it to separate information out of it. The expanding volume of human services information created through IoT gadgets, electronic health, mobile health, and telemedicine screening requires the advancement of new strategies and approaches for their taking care of. In this chapter, we discuss a portion of the healthcare challenges and information analysis development. To screen the health status of an individual, support from sensors and IoT gadgets is fundamental. The goal of this examination is to give healthcare services administrations to the sick just as sound populace through remote observation utilizing keen calculations, instruments, and methods with quicker investigation and master intervention for better treatment suggestions. The analysis is done on the Blood Pressure data and Heart Disease dataset by collecting the data from the IoT sensors and the framework is able to predict the disease. It can likewise be gainful for distantly checking chronic diseases, which require essential physical data, biological, and hereditary information.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kusiak, A., Dixonb, B., Shaha, S.: Predicting survival time for kidney dialysis patients: a data mining approach. Comput. Biol. Med. 35, 311–327 (2005). Elsevier Publication
Abhishek, G.S.M.T., Gupta, D.: Proposing efficient neural network training model for kidney stone diagnosis. Int. J. Comput. Sci. Inf. Technol. 3(3), 3900–3904 (2012)
Ashfaq Ahmed, K., Aljahdali, S., Hussain, S.N.: Comparative Prediction performance with support vector machine and random forest classification techniques. Int. J. Comput. Appl. 69(11), 12–16 (2013)
Kara, S., Guvenb, A., Urk Onerc, A.O.: Utilization of artificial neural networks in the diagnosis of optic nerve diseases. Comput. Biol. Med. 36, 428–437 (2006). Elsevier Publication
Sweety Bakyarani, E., Srimathi. H., Bagavandas, M.: A survey of machine learning algorithms in health care. Int. J. Sci. Technol. Res. 8(11). ISSN 2277-8616
Shinde, P., Jadhav, S.: Int. J. Comput. Sci. Inf. Technol. 5(3), 3928–3933 (2014)
Sarwar, M.U., Hanif, M.K., Talib, R., Mobeen, A., Aslam, M.: A survey of Big Data analytics in healthcare. Int. J. Adv. Comput. Sci. Appl. 8(6) (2017)
Padmashree, T., Cauvery, N.K., Anirudh, V.C, Kumar, P.: Int. J. Innov. Eng. Technol. (IJIET) 8(1) (2017). ISSN 2319-1058
Abidi, S.S.R., Abidi, S.R.: Intelligent health data analytics: a convergence of artificial intelligence and big data Healthcare Management Forum 1-5 ª2019. The Canadian College of Health Leaders (2019)
Islam, M.S., Hasan, M.M., Wang, X., Germack, H.D., Noor-E-Alam: A systematic review on healthcare analytics: application and theoretical perspective of data mining. Healthcare, 6, 54 (2018). 10.3390/healthcare6020054
Pentek, I., Adamko, A.: Hungary bio-sensory data warehouse with analytics for e-health solutions. In: 10th IEEE International Conference on Cognitive Infocommunications—CogInfoCom 2019 October 23–25, 2019 Naples, Italy (2019)
Isravel, D.P., Vidya Priya Darcini, S., Silas, S.: Improved heart disease diagnostic IoT model using machine learning techniques. Int. J. Sci. Technol. Res. 9(02) (2020). ISSN 2277-8616
Rastogi, R., Chaturvedi, D.K., Satya, S., Arora, N.: Intelligent heart disease prediction on physical and mental parameters: a ML based IoT and big data application and analysis. In: Machine Learning with Health Care Perspective: Machine Learning and Healthcare, pp. 199–236. Springer International Publishing (2020)
Dinh, A., Luu, L., Cao, T.: Blood pressure measurement using finger ECG and photoplethysmogram for IoT. In: 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6) 2018, pp. 83–89. Springer, Singapore (2018)
Kirtana, R.N., Lokeswari, Y.V.: IEEE International Conference on Computer, Communication, and Signal Processing (ICCCSP-2017) 978-1-5090-3716-2/17/$31.00 ©2017 IEEE (2017)
Blake, C.L., Merz, C.J.: Repository of machine learning databases, University of California, Irvine. http://www.ics.uci.edu/∼mlearn/mlrepository.html,1998 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Sunagar, P., Hanumantharaju, R., Pradeep Kumar, D., Sowmya, B.J., Seema, S., Kanavalli, A. (2021). Smart Healthcare: Using IoT and Machine Learning-Based Analytics. In: Srinivasa, K.G., G. M., S., Sekhar, S.R.M. (eds) Artificial Intelligence for Information Management: A Healthcare Perspective. Studies in Big Data, vol 88. Springer, Singapore. https://doi.org/10.1007/978-981-16-0415-7_15
Download citation
DOI: https://doi.org/10.1007/978-981-16-0415-7_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0414-0
Online ISBN: 978-981-16-0415-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)