## 4 Discussion  
Little academic progress has been made in the analysis of acute and chronic pain using infrared thermography. In particular, identifying the exact location of a neuromuscular lesion through thermographic imaging without radiological information from MRI or CT may have different results depending on the skill of the physician; hence, an ML method can be helpful. Therefore, previous studies by the authors have reported a method for differentially diagnosing lumbosacral radiiculopathy of HLD L4/5 and L5/S1 as one of the most frequently herniated discs, using feature extraction techniques with a bag-of-words feature extraction algorithm [17]. The location of a patient's pain can be easily diagnosed using an infrared thermal image to compare the temperature difference between the left and right sides and by identifying the area with the most significant temperature change [18]. A previous study reported that the temperature difference in the lower limbs decreased when pain improved postoperatively, and several studies have reported that the temperature changes along with pain at the location of the lesion [19]. According to these prior studies, the greater the degree of pain, the greater the temperature difference in the thermographic image. Nevertheless, this study was the first to classify the severity of pain by integrating the patient's sex, age, duration of pain, and nonlinear features using ML. The temperature difference occurs because the greater the pressure on the nerve root following the herniated disc, the higher the intensity of pain in the patient. This low-temperature phenomenon intensifies. Moreover, the sympathetic nerve activity of the autonomic reflex arc may change according to the change in the amount of information transmitted to the retrodural nerve. The case comparison reported here demonstrates an evident thermal difference depending on the severity of pain in acute lumbosacral radiiculopathy (Figure 4). [PERSON] et al. reported a correlation in which the temperature difference between the left and right sides increased as disc herniation became more severe [11]. As the degree of pain decreased, the temperature difference also decreased. In some cases, pain persists, although the temperature difference decreases; cases where there are pain and temperature increases in the case of trauma have also been reported [12]. The degree of pain has a robust subjective tendency that varies depending on the individual's experience, environment, secondary gain, and education level. Because the pain and local low-temperature phenomenon may fluctuate, classifying and evaluating the severity of pain using only infrared body temperature images taken at a specific time may be difficult for nonskilled medical staff. Therefore, if the ML method performed in this study is applied by integrating and applying other clinical characteristics as variables through a detailed history, including the period of pain, the temperature distribution characteristics observed in the infrared body temperature image and nonlinear analysis can be applied, which can be clinically helpful in determining the severity of pain. The RF model demonstrated the best performance for the F1 score, whereas the SVM model demonstrated the best performance for specificity (Table 2 and Figure 2). To improve accuracy, the developed ML models need to be calibrated, with, for example, an isotonic calibration for generality (Figure 3). The authors evaluated why RF's results outperformed several other algorithms. Perhaps, since it has a bootstrap ensemble method, RF is advantageous for small perturbations in the data. Moreover, due to the implicit feature selection mechanism, the representation becomes resilient to noninformative or noisy features. Additionally, the RF algorithm derives task-specific similarity concepts for target information in contrast to classical hierarchical clustering approaches. The current study has several advantages.