Prediction model for psychological disorders in ankylosing spondylitis patients based on multi-label classification.

Objective: This study aims to develop a predictive model to assess the likelihood of psychological disorders in patients with ankylosing spondylitis (AS) and to explore the relationships between different factors and psychological disorders. Methods: Patients were randomly divided into training and test sets in an 8:2 ratio. The Boruta algorithm was applied to select predictive factors, and a multi-label classification learning algorithm based on association rules (AR) was developed. Models were constructed using Random Forest (RF), K-Nearest Neighbor (KNN), RF-AR, and KNN-AR, and their performance was assessed through receiver operating characteristic (ROC) curves on the test set. Results: A total of 513 AS patients were included, with 410 in the training set and 103 in the test set. The Boruta algorithm identified five key variables for the model: fatigue, ASAS-HI score, disease duration, disease activity, and BMI. The RF-AR model performed best, with an accuracy of 0.89 ± 0.06, recall of 0.78 ± 0.1, F1-score of 0.86 ± 0.08, Hamming loss of 0.05 ± 0.03, and a Jaccard similarity coefficient of 0.75 ± 0.12. The area under the curve (AUC) for the training set was 0.94. Conclusion: This study developed a predictive model for assessing the risk of psychological disorders in AS patients. The model effectively captures the presence of psychological disorders, providing clinicians with valuable insights for adjusting treatment strategies.