Next Article in Journal
The Driving Force Analysis of NDVI Dynamics in the Trans-Boundary Tumen River Basin between 2000 and 2015
Next Article in Special Issue
Assessing the Value of Housing Schemes through Sustainable Return on Investment: A Path towards Sustainability-Led Evaluations?
Previous Article in Journal
Consumer Acceptance Analysis of the Home Energy Management System
Previous Article in Special Issue
Micro-Study of the Evolution of Rural Settlement Patterns and Their Spatial Association with Water and Land Resources: A Case Study of Shandan County, China
Open AccessReview

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.
Sustainability 2017, 9(12), 2309; https://doi.org/10.3390/su9122309
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)
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
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop