Machine Learning Approaches for Analysis in Smart Healthcare Informatics
Nasmin Jiwani, Ketan Gupta, Pawan Whig
Abstract
As the average age rises, so does the demand for healthcare services to be provided and improved. The development of information and communication technology has led to the emergence of insolent capitals. Smart Health (s-Health) is one of those components, which is utilized to improve healthcare by offering a variety of facilities, for instance nursing, initial illness analysis. There are numerous ML approaches available now which can help with s-Health facilities. This chapter examines current published studies in the field of smart health from 2010 to 2021, as well as a methodical examination of the numerous ML methodologies used in smart-Health. These results indicate that deep learning is used in a range of s-Health activities, such as glaucoma detection, Alzheimer's disease, bacteria infection detection, ICU hospitalizations, and cataracts identification. An Artificial Neural Network, Vector Support Machines method, and deep learning, notably the Convolutional Neural Network, are the most extensively used deep learning methods, and they all function well in most circumstances.