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Using machine learning approach to predict depression and anxiety among patients with epilepsy in China: A
Zihan Wei1, Xinpei Wang2, Lei Ren3
1Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, People's Republic of China.
Background:
Anxiety and depression are the most prevalent comorbidities among epilepsy patients. The screen and diagnosis of anxiety and depression are quite important for the management of patients with epilepsy. In that case, the method for accurately predicting anxiety and depression needs to be further explored.
Methods:
A total of 480 patients with epilepsy (PWE) were enrolled in our study. Anxiety and Depressive symptoms were evaluated. Six machine learning models were used to predict anxiety and depression in patients with epilepsy. Receiver operating curve (ROC), decision curve analysis (DCA) and moDel Agnostic Language for Exploration and eXplanation (DALEX) package were used to evaluate the accuracy of machine learning models.
Results:
For anxiety, the area under the ROC curve was not significantly different between models. DCA revealed that random forest and multilayer perceptron has the largest net benefit within different probability threshold. DALEX revealed that random forest and multilayer perceptron were models with best performance and stigma had the highest feature importance. For depression, the results were much the same.
Conclusions:
Methods created in this study may offer much help identifying PWE with high risk of anxiety and depression. The decision support system may be valuable for the everyday management of PWE. Further study is needed to test the outcome of applying this system to clinical settings.
