Role of machine learning and big data in healthcare for the prediction of epidemic diseases: a survey. (28th May 2021)

• Record Type: Journal Article
• Authors: Sharma, S.; Gupta, Yogesh Kumar
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 12:Number 2/3(2021)
• Page Start: 148
• Page End: 155
• Publication Date: 2021-05-28
• Subjects: machine learning -- epidemiology and epidemic diseases -- big data -- surveillance on web -- COVID-19; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

Epidemic diseases are the contagious or infectious diseases which are possible to be spread into the entire country, and are defined as an outbreak that occurs and affects an exceptionally high proportion of the population. However, these infectious ailments if controlled beforehand by using trending technologies for the early prediction would not turn into mortality situations. With this view, this paper is summarising the research work by using machine learning and big data handling techniques for the early prediction of epidemic diseases. The epidemic diseases especially covered in this review are influenza, malaria and dengue ailment. The diseases are compared against machine learning models used and input data contemplated. An observation for the prediction of diseases found that same factors associated with searching techniques give different results for different locations; overall searches are showing diversity and dearth in data. Moreover, dearth of data will mitigate the accuracy.