Classification of infectious and noninfectious diseases using artificial neural networks from 24-hour continuous tympanic temperature data of patients with undifferentiated fever

Fever is one of the major clinical symptoms of undifferentiated fever cases. Early diagnosis of undifferentiated fever is a challenging task for the physician. The aim of this study was to classify infectious and noninfectious diseases from 24-hour continuous tympanic temperature recordings of patients with undifferentiated fever using a machine learning algorithm (artificial neural network). This was an observational study conducted in 103 patients who presented with undifferentiated fever. Twenty-four–hour continuous tympanic temperature was recorded from each patient. Features were extracted from temperature signals and classified into infectious and noninfectious diseases using an artificial neural network (ANN). The ANN classifier provided the highest accuracy at 91.3% for differentiating infectious and noninfectious diseases from undifferentiated fever cases. Significant kappa agreement (κ = 0.777) was found between the final diagnosis as determined by the physician and the classification obtained using an ANN classifier. Based on our results, we conclude that the continuous 24-hour temperature monitoring and application of an ANN classifier provides a simple noninvasive and inexpensive supplementary diagnostic method to differentiate infectious and noninfectious diseases.