Comparative analysis of forecasting models in healthcare (COVID-19)

A. K. Awasthi; Minakshi Sharma

doi:10.53730/ijhs.v6nS3.8055

Authors

A. K. Awasthi Department of Mathematics, School of Chemical Engineering & Physical Sciences, Lovely Professional University, Phagwara, Punjab, 144411, India
Minakshi Sharma
minakshisohareya@gmail.com
Department of Mathematics, School of Chemical Engineering & Physical Sciences, Lovely Professional University, Phagwara, Punjab, 144411, India

Keywords:

data mining, knowledge discovery in databases, healthcare analysis, spss, python

Abstract

Knowledge discovery in databases (KDD) is another name of Data mining. It is an interdisciplinary area which focuses on extraction of useful knowledge from data in every sector like health, education, business etc. There are many fields to explore like business, health care, e-commerce etc but nowadays, as covid pandemic is affecting everyone and due to surge in coronavirus cases causing shortage of hospital beds, oxygen supplies, vaccine and turning away patients from hospitals, put creaky health infrastructure in spotlight. The plenty of data is available in the medical field of these conditions. To analyse the problems, there are many data mining approaches which can be used to extract useful patterns from these types of data to follow the upcoming trends. This study is to compare the various models like KNN, improved RF model and multilayer perceptron by using SPSS and python software. The data of COVID-19 has been taken from Kaggle’s website which is based on the symptoms and the forecasted results has been shown.

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