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.
| Original language | English |
|---|---|
| Article number | 2309 |
| Journal | Sustainability (Switzerland) |
| Volume | 9 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 18 Dec 2017 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
-
SDG 7 Affordable and Clean Energy
-
SDG 11 Sustainable Cities and Communities
Keywords
- Automation
- Computational intelligence
- Data analysis
- Data mining
- Disease diagnosis
- Healthcare
- Smart city
- Smart living
- Social progress
- Sustainability
Fingerprint
Dive into the research topics of 'Disease diagnosis in smart healthcare: Innovation, technologies and applications'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver