| Title |
Research on Identification and Classification of Depression in College Students through Feature Analysis |
| Authors |
(Yachai Sun) ; (Haixia Zhang) ; (Jiyu Men) |
| DOI |
https://doi.org/10.5573/IEIESPC.2023.12.6.511 |
| Keywords |
Feature analysis; Depression identification; Classifier algorithm |
| Abstract |
The facial expression features of experimental subjects were analyzed to explore the differences in facial expression between patients with psychological depression and those with a healthy psychology. Three psychological depression identification and classification models, the K-nearest neighbor algorithm, support vector machine, and logistic regression, were constructed to verify the usability of a classification model through facial expression feature analysis and identify college students with psychological depression. The experimental results showed significant differences in the average values of several facial expressions, including AU4, AU7, AU9, AU12, AU18, and AU27. The P values were less than 0.05, so they were used in subsequent model analysis. The results obtained through identification by the classification model showed that the performance of the K-nearest neighbor algorithm in the mean absolute error (MAE), root mean square error (RMSE), and accuracy were the poorest, followed by the logistic regression and support vector machine. The MAE, RMSE, and accuracy of the K-nearest neighbor algorithm were 7.14, 9.37, and 83.61%, respectively; the values of the logistic regression algorithm were 6.83, 8.79, and 91.97%, respectively; the values of the support vector machine were 6.23, 7.76, and 94.80%, respectively. This study showed that a classification model constructed using facial expression features could be used to identify and classify college students with psychological depression. |