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Sustainability 2017, 9(12), 2309; doi:10.3390/su9122309

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

1
Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
2
Virtual Reality Research Center, Effat University, Jeddah 21577, Saudi Arabia
3
School of Biological Sciences, Faculty of Science, The University of Hong Kong, Hong Kong, China
4
Department of Business Administration and Accountability, Faculty of Economics, The University of Oviedo, 33003 Oviedo, Spain
5
Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China
6
School of Information Science & Technology, Donghua University, Shanghai 200051, China
*
Author to whom correspondence should be addressed.
Received: 18 October 2017 / Revised: 7 December 2017 / Accepted: 8 December 2017 / Published: 18 December 2017
(This article belongs to the Special Issue Sustainable Smart Cities and Smart Villages Research)
View Full-Text   |   Download PDF [518 KB, uploaded 18 December 2017]   |  

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. View Full-Text
Keywords: automation; computational intelligence; data analysis; data mining; disease diagnosis; healthcare; smart living; smart city; social progress; sustainability automation; computational intelligence; data analysis; data mining; disease diagnosis; healthcare; smart living; smart city; social progress; sustainability
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MDPI and ACS Style

Chui, K.T.; Alhalabi, W.; Pang, S.S.H.; Pablos, P.O.; Liu, R.W.; Zhao, M. Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications. Sustainability 2017, 9, 2309.

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