Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

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...

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Bibliographic Details
Published in:Sustainability 2017-12, Vol.9 (12), p.2309
Main Authors: Chui, Kwok, Alhalabi, Wadee, Pang, Sally, Pablos, Patricia, Liu, Ryan, Zhao, Mingbo
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Cardiovascular diseases
Combinatorial analysis
Decision making
Dementia disorders
Diabetes mellitus
Diagnosis
Evolutionary algorithms
Health care
Information technology
Innovations
Internet of Things
Learning algorithms
Medical diagnosis
Optimization
Stochasticity
Sustainability
Sustainable development
Tuberculosis
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su9122309