A new method for assessing chronic insomnia: Machine learning-based fusion of TCM observation digital features.

Background: In the clinical assessment of chronic insomnia, modern medicine encounters challenges in the widespread adoption of objective assessment methods, such as polysomnography, due to their high costs and complex operational requirements. Therefore, it was crucial to implement objective and varied methods to accurately assess chronic insomnia. Methods: Clinical information was collected from 594 patients diagnosed with chronic insomnia. Their facial and tongue features, as observed through traditional Chinese medicine, were recorded using the tongue face diagnosis analysis-1 instrument and analyzed with the tongue/face diagnosis analysis system. Stepwise-regression analysis, principal component analysis, and zero-inflated negative binomial (ZINB) regression were employed for variable screening. Ultimately, the classification model for assessing the severity of chronic insomnia was constructed using six supervised machine learning methods: decision trees, neural networks, random forests, support vector machines, logistic regression, and naive Bayes. The model was evaluated using sensitivity, specificity, F1 score, precision, and accuracy. Visualization was conducted with the Shapley Additive exPlanations (SHAP) explainer and decision curve analysis (DCA), and it was finally calibrated using Platt scaling. Results: A comprehensive evaluation revealed that model 4 exhibited superior performance. This model integrated baseline data, sleep symptoms, and facial features, achieving its highest receiver operating characteristic curve value of 0.822. Furthermore, DCA demonstrated that model 4 exhibited significant clinical utility. The SHAP illustrated that among the variables exerting the greatest influence on insomnia, the effects of Ch-Y and CH-G were notably prominent, compared to the conventional PSQI and SAS, followed by Ch-R. Finally, the results of Platt scaling calibration indicated that model 4 exhibited significant improvement post-calibration, with the predicted probabilities closely aligning with the actual probabilities of occurrence. Conclusions: This study emphasized convenient and non-invasive diagnostic methods for investigating specific facial and tongue features associated with chronic insomnia. A classification model for chronic insomnia has been developed through the integration of multiple features, enabling a more accurate assessment of insomnia severity and facilitating advancements in therapeutic interventions.