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Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Author

Listed:
  • Kwok Tai Chui

    (Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China)

  • Wadee Alhalabi

    (Virtual Reality Research Center, Effat University, Jeddah 21577, Saudi Arabia)

  • Sally Shuk Han Pang

    (School of Biological Sciences, Faculty of Science, The University of Hong Kong, Hong Kong, China)

  • Patricia Ordóñez de Pablos

    (Department of Business Administration and Accountability, Faculty of Economics, The University of Oviedo, 33003 Oviedo, Spain)

  • Ryan Wen Liu

    (Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China)

  • Mingbo Zhao

    (School of Information Science & Technology, Donghua University, Shanghai 200051, China)

Abstract

To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.

Suggested Citation

  • Kwok Tai Chui & Wadee Alhalabi & Sally Shuk Han Pang & Patricia Ordóñez de Pablos & Ryan Wen Liu & Mingbo Zhao, 2017. "Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications," Sustainability, MDPI, vol. 9(12), pages 1-23, December.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:12:p:2309-:d:123284
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    2. Xinyue Zhang & Xiaolu Gao & Danxian Wu & Zening Xu & Hongjie Wang, 2021. "The Role of Big Data in Aging and Older People’s Health Research: A Systematic Review and Ecological Framework," Sustainability, MDPI, vol. 13(21), pages 1-19, October.
    3. Ali Alhaij & Bassem Jamoussi & Asad Abu-Rizaiza, 2023. "The Development of a Life-Cycle-Based Sustainability Index That Incorporates Patient-Centredness for Assessing and Reporting the Sustainability of Healthcare Buildings in Saudi Arabia," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    4. Popkova, Elena G. & Sergi, Bruno S., 2022. "Digital public health: Automation based on new datasets and the Internet of Things," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    5. Bassem Jamoussi & Asad Abu-Rizaiza & Ali AL-Haij, 2022. "Sustainable Building Standards, Codes and Certification Systems: The Status Quo and Future Directions in Saudi Arabia," Sustainability, MDPI, vol. 14(16), pages 1-24, August.
    6. Jianwei Deng & Sibo Huang & Liuan Wang & Wenhao Deng & Tianan Yang, 2022. "Conceptual Framework for Smart Health: A Multi-Dimensional Model Using IPO Logic to Link Drivers and Outcomes," IJERPH, MDPI, vol. 19(24), pages 1-22, December.
    7. Anna Visvizi & Miltiadis D. Lytras, 2018. "It’s Not a Fad: Smart Cities and Smart Villages Research in European and Global Contexts," Sustainability, MDPI, vol. 10(8), pages 1-10, August.
    8. Kyungtae Kim & Sungjoo Lee, 2018. "How Can Big Data Complement Expert Analysis? A Value Chain Case Study," Sustainability, MDPI, vol. 10(3), pages 1-21, March.
    9. Faramarz Khosravi & Gokhan Izbirak & Kehinde Adewale Adesina, 2019. "An Exponentially Distributed Stochastic Model for Sustainability Measurement of a Healthcare System," Sustainability, MDPI, vol. 11(5), pages 1-16, March.
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    11. Michela Arnaboldi, 2018. "The Missing Variable in Big Data for Social Sciences: The Decision-Maker," Sustainability, MDPI, vol. 10(10), pages 1-18, September.

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