The rapid progress in domains like machine learning, and big data has created plenty
of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence
in healthcare can result in breakthroughs like precise disease diagnosis, novel methods
of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare
costs. The implementation of machine intelligence algorithms on the massive healthcare datasets
is computationally expensive. However, consequential progress in computational power
during recent years has facilitated the deployment of machine intelligence algorithms in
healthcare applications. Motivated to explore these applications, this paper presents a review of research
works dedicated to the implementation of machine learning on healthcare datasets. The studies
that were conducted have been categorized into following groups (a) disease diagnosis and detection,
(b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic
outbreak prediction. The objective of the research is to help the researchers in this field to get a
comprehensive overview of the machine learning applications in healthcare. Apart from revealing
the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced
research in the domain of machine intelligence-driven healthcare.
Presenting evaluation results from the usage of the inCASA Remote Healthcare Monitoring Platform
