Frontiers in public health · 2025
Prediction model for psychological disorders in ankylosing spondylitis patients based on multi-label classification.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Yang Kun, Gong Yifan, Xu Xiaohan, Sun Tiantian, Qu Xinning, He Xiaxiu, Liu Hongxiao
Аннотация
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.
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.