Classification and Forecasting Model for Covid -19 Disease Severities based on Medical Diagnosis using Weighted Average Dynamic Time Warping Technique

Abstract

Due to Covid-19 pandemic, mankind was affected with respect to mental and physical stress. The causes of the disease have to be analysed based on the correlation factors such as Medication, Age, Gender, physical fitness and habits. In this paper we have proposed a classification model based on weighted average dynamic time warping approach to detect the disease severity as High, Medium, and Low by considering the multi-variant dependent variables that affect the prediction of Covid-19 positive cases. We also proposed the forecasting model based on the time series exponential moving average to identify the growth of disease with respect to Age, Gender and Medical history of the covid-19 positive patient. The results are obtained by defining the correlation function to measure the disease severity in the range of High, Medium and Low. The time series analysis is done with respect to mean average disease severity and also number of positive cases. The forecasting is performed based on the age, gender and existing disorders in health. The results are analysed with other time series classification models such as weighted time wrapping to make the model fits maximum to the available input.