Thermal Image-Based Temperament Classification by Genetic Algorithm and Adaboost Classifier

Background: Temperament (Mizaj) determination is an important stage of diagnosis in Persian Medicine. This study aimed to evaluate thermal imaging as a reliable tool that can be used instead of subjective assessments. Methods: The temperament of 34 participants was assessed by a PM specialist using standardized Mojahedi Mizaj Questionnaire (MMQ) and thermal images of the wrist in the supine position, the back of the hand, and their whole face under supervision of the physician were recorded. Thirteen thermal features were extracted and a classifying algorithm was designed based on the genetic algorithm and Adaboost classifier in reference to the temperament questionnaire. Results: The results showed that the mean temperature and temperature variations in the thermal images were relatively consistent with the results of MMQ. Among the three body regions, the results related to the image from Malmas were most consistent with MMQ. By selecting six of the 13 features that had the most impact on the classification, the accuracy of 94.7 +/- 13.0, sensitivity of 95.7 +/- 11.3, and specificity of 98.2 +/- 4.2 were obtained. Conclusions: The thermal imaging was relatively consistent with standardized MMQ and can be used as a reliable tool for evaluating warm/cold temperament. However, the results reveal that thermal imaging features may not be only main features for temperament classification and for more reliable classification, it needs to add some different features such as wrist pulse features and some subjective characteristics.