Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand

Abstract

With the constant mutation of COVID-19 variants, the need to reduce the spread should be explored. MorChana is a mobile application utilized in Thailand to help mitigate the spread of the virus. This study aimed to explore factors affecting the actual use (AU) of the application through the use of machine learning algorithms (MLA) such as Random Forest Classifier (RFC) and Artificial Neural Network (ANN). An integrated Protection Motivation Theory (PMT) and the Unified Theory of Acceptance and Use of Technology (UTAUT) were considered. Using convenience sampling, a total of 907 valid responses from those who answered the online survey were voluntarily gathered. With 93.00% and 98.12% accuracy from RFC and ANN, it was seen that hedonic motivation and facilitating conditions were seen to be factors affecting very high AU; while habit and understanding led to high AU. It was seen that when people understand the impact and causes of the COVID-19 pandemic’s aftermath, its severity, and also see a way to reduce it, it would lead to the actual usage of a system. The findings of this study could be used by developers, the government, and stakeholders to capitalize on using the health-related applications with the intention of increasing actual usage. The framework and methodology used presented a way to evaluate health-related technologies. Moreover, the developing trends of using MLA for evaluating human behavior-related studies were further justified in this study. It is suggested that MLA could be utilized to assess factors affecting human behavior and technology used worldwide.

Determining factors affecting the perceived usability of air pollution detection mobile application “AirVisual” in Thailand: A structural equation model forest classifier approach

“…(2021a) IU3 I would always install the AirVisual mobile application on my mobile phone when I change my mobile phone in the future. ( Chuenyindee et al., 2022 ); ( Ong et al., 2022 ) IU4 I plan to use the AirVisual mobile application for checking air quality index when I see the weather is different (presence of fog). Perceived Usability PU1 I found the AirVisual mobile application very comfortable to use.…”

Socio-Economic Factors Affecting Member’s Satisfaction towards National Health Insurance: An Evidence from the Philippines

“…The collected data for this study were from 500 Filipino respondents, which presented a total of 23,500 datasets (500 × 47) for predicting factors affecting the members’ satisfaction with PhilHealth. The only response from the Likert Scale survey was considered in this study following the study of Ong et al [ 71 ]. In accordance, Yuduang et al [ 72 ] utilized a similar methodology following a data pre-processing stage.…”

“…Data preparation was performed before running the MLA, adopted from different studies [ 71 , 72 ]. This study utilized SPSS 25 to review the data and found that there were no missing data, and the data were pre-processed.…”

“…Deep Learning Neural Network (DLNN) is a type of Neural Network (NN) used due to its great ability to generalize and classify data [ 71 , 72 ]. DLNN can mimic the structures of the human brain and central nervous system to recognize hidden patterns among datasets [ 72 ].…”

“…DLNN can mimic the structures of the human brain and central nervous system to recognize hidden patterns among datasets [ 72 ]. It has at least two hidden layers of patterns, containing an input layer and an output layer responsible for prediction [ 71 ]. Furthermore, DLNN has also demonstrated excellent performance in many classification-related medical imaging applications [ 73 ].…”