Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

TLDR

Smart city vision merges AI, big data, ICT, and IoT, and the ageing population drives the need for innovative smart healthcare technologies. The paper reviews disease diagnosis in smart healthcare. The review covers emerging optimization and machine learning algorithms—evolutionary, stochastic, combinatorial—and their application to disease diagnosis in cardiovascular disease, diabetes, Alzheimer’s, dementia, and tuberculosis. The review discusses challenges to deploying disease diagnosis in healthcare.

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.