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Chui, K. T.; Alhalabi, W.; Pang, S. S. H.; Ordóñez de Pablos, Patricia; Liu, R. W.; Zhao, M. (2017)
Publisher: MDPI AG
Languages: English
Types: Article
Subjects: data analysis, TD194-195, Renewable energy sources, data mining, social progress, sustainability, healthcare, computational intelligence, TJ807-830, automation, smart living, GE1-350, disease diagnosis, Environmental sciences, Environmental effects of industries and plants, smart city
Identifiers:doi:10.3390/su9122309
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.
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