Machine‐learning classification of bipolar disorder incorporating wearable‐derived core body temperature and actigraphy‐derived sleep indices

Aim This study aimed to classify patients with bipolar disorder (BD) and normal controls (NCs) using machine‐learning models that incorporate wearable‐derived core body temperature (CBT) and actigraphy‐derived sleep indices. Methods We enrolled 23 euthymic patients with BD and 43 NCs. Sleep parameters were collected via wrist actigraphy for 14 days, and CBT was measured with a wearable device for 3 days to estimate the CBT nadir and its phase differences with sleep indices. Models were constructed with a base model using CBT‐ and sleep‐derived features, and an extended model that included sociodemographic variables. Features were standardized (StandardScaler), and classifiers (random forest, LightGBM, and XGBoost) were evaluated. Hyperparameters were optimized using cross‐validation within the training data. Performance was evaluated using repeated nested cross‐validation. SHapley Additive exPlanations (SHAP) values were computed in the extended model to quantify relative feature contributions to the model output. Results In nested cross‐validation at the MaxF1 operating point, the mean area under the receiver operating characteristic curve (ROC–AUC) was 0.771 ± 0.162 for the base model and 0.930 ± 0.050 for the extended model. In the extended model, SHAP suggested that total sleep time, wake time, and the wake time–CBT nadir phase difference were features potentially associated with the model output. Conclusion In this small‐sample study, a classification approach combining wearable‐derived CBT indices and actigraphy‐based sleep parameters suggested preliminary discrimination between BD and NC. Further validation is warranted in larger cohorts with balanced background characteristics, including disorders requiring differential diagnosis.