  The COVID-19 pandemic has underscored the necessity for advanced diagnostic
tools in global health systems. Infrared Thermography (IRT) has proven to be a
crucial non-contact method for measuring body temperature, vital for
identifying febrile conditions associated with infectious diseases like
COVID-19. Traditional non-contact infrared thermometers (NCITs) often exhibit
significant variability in readings. To address this, we integrated machine
learning algorithms with IRT to enhance the accuracy and reliability of
temperature measurements. Our study systematically evaluated various regression
models using heuristic feature engineering techniques, focusing on features'
physiological relevance and statistical significance. The Convolutional Neural
Network (CNN) model, utilizing these techniques, achieved the lowest RMSE of
0.2223, demonstrating superior performance compared to results reported in
previous literature. Among non-neural network models, the Binning method
achieved the best performance with an RMSE of 0.2296. Our findings highlight
the potential of combining advanced feature engineering with machine learning
to improve diagnostic tools' effectiveness, with implications extending to
other non-contact or remote sensing biomedical applications. This paper offers
a comprehensive analysis of these methodologies, providing a foundation for
future research in the field of non-invasive medical diagnostics.
