Predicting the motivators of telemedicine acceptance in COVID-19 pandemic using multiple regression and ANN approach

Abstract

Since the outbreak of COVID-19, healthcare professionals and patients widely accepted the usages of Telemedicine. Though it has many potential benefits but still lacks appreciation on large scale. In this research work, Multiple regression and Artificial Neural network (ANN) techniques are used to examine the data collected from 146 patients through a structured questionnaire for the period since March to June-2020 during the COVID-19 pandemic period. It was observed that ANN model is a better predictor for predicting with a very accuracy in comparison multiple regressions in terms of nonlinearity and linearity. The findings of this research work will have broad implications for the healthcare industry and practitioners who can use the outcome in further implementation and adoption of Telemedicine.

Subject Classification:

• 62H86 (Multivariate analysis and fuzziness)

Keywords:

• Telemedicine
• TAM model
• ANN
• Multiple regression analysis (MRA)
• World Health Organization (WHO)
• Pandemic
• COVID-19
• Feed forward back propagation (FFBP)