A review on the significance of body temperature interpretation for early infectious disease diagnosis

Abstract

Infectious diseases have always been a serious discussion worldwide due to their disease elevation leading to mortality. Although vaccination and prevention exist for this kind of disease, the severity is still in many countries, especially COVID-19. Diagnosing these diseases typically relies on time-consuming laboratory tests, conventional vital sign monitoring, and visual inspection by medical practitioners in hospitals. However, these time-consuming clinical procedures will cause a delay in diagnosis while increasing the risk of transmission in the community. Besides, most of the vital sign monitor and associated analysis software available in the commercial medical device market do not support infectious disease screening based on state-of-the-art algorithms as a clinical decision support system. As a result, research has been proposed to apply various techniques to diagnose and differentiate infectious diseases by focusing on clinical presentations, laboratory parameters, and temperature time series data. While most studies strongly justify clinical presentations and laboratory parameters could provide good diagnosing efficiency, some research also extends the possibility of early disease diagnosis based on body temperature alone. On the other hand, many studies proposed incorporating mobile technologies and machine learning which is suitable for early health monitoring at home. This paper discusses all the possible techniques in clinical and laboratory parameter analysis, body temperature data configuration, and symptoms mapping for infectious diseases diagnosis. The adequate data preprocessing, statistical analysis, machine learning models, performance metrics and mobile technologies implementation for disease diagnosis are also discussed to identify the best solution for effective infectious disease screening.