Abstract
Under the new trend of industry 4.0 software-defined network, the value of meta heuristic algorithm was explored in the recognition of depression in patients with androgenic alopecia (AGA), and there was an analysis on the effect of comprehensive psychological interventions in the rehabilitation of AGA patients. Based on the meta heuristic algorithm, the Filter and Wrapper algorithms were combined in this study to form a new feature selection algorithm FAW-FS. Then, the classification accuracy of FAW-FS and the ability to identify depression disorders were verified under different open data sets. 54 patients with AGA who went to the Medical Cosmetic Center of Tongji Hospital were selected as the research objects and rolled into a control group (routine psychological intervention) and an intervention group (routine + comprehensive psychological interventions) according to different psychological intervention methods, with 27 cases in each group. The differences of the self-rating anxiety scale (SAS), self-rating depression scale (SDS), Hamilton depression scale (HAMD), and physical, psychological, social, and substance function scores before and after intervention were compared between the two groups of AGA patients, and the depression efficacy and compliance of the two groups were analyzed after intervention. The results showed that the classification accuracy of FAW-FS algorithm was the highest in logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN) algorithm, support vector machine (SVM) algorithm, and random forest (RF) algorithm, which was 80.87, 79.24, 80.42, 83.07, and 81.45%, respectively. The LR algorithm had the highest feature selection accuracy of 82.94%, and the classification accuracy of depression disorder in RF algorithm was up to 73.01%. Besides, the SDS, SAS, and HAMD scores of the intervention group were lower sharply than the scores of the control group (p < 0.05). The physical function, psychological function, social function, and substance function scores of the intervention group were higher markedly than those of the control group (p < 0.05). In addition, the proportions of cured, markedly effective, total effective, full compliance, and total compliance patients in the intervention group increased obviously in contrast to the proportions of the control group (p < 0.05). Therefore, it indicated that the FAW-FS algorithm established in this study had significant advantages in the recognition of depression in AGA patients, and comprehensive psychological intervention had a positive effect in the rehabilitation of depression in AGA patients.
Highlights
Androgenetic alopecia (AGA) is a kind of hair loss skin disease which is characterized by non-scarring and progressive hair follicle miniaturization
The classification accuracy of FAW-FS algorithm established in this study was compared with Correlation Attribute Eval (CA), Gain Ratio Attribute Eval (GR), Relief FAttribute Eval (RF), simulated annealing (SA) algorithm, and genetic algorithm (GA) in the feature selection of logistic regression (Figure 3)
A depression EEG signal recognition model FAWFS was established based on deep learning meta-heuristic algorithm, which was applied to the recognition of depression EEG signals in patients with AGA
Summary
Androgenetic alopecia (AGA) is a kind of hair loss skin disease which is characterized by non-scarring and progressive hair follicle miniaturization. It is a common clinical skin disease, mainly characterized by shortening of hair follicle growth period, terminal hair follicle miniaturization, and progressive thinning of hair. A large number of investigation reports and meta-analysis prompts to inquire about the cognitive evaluation, emotional expression, and response of different patients to their diseases. They have received the necessary psychological interventions, so as to establish scientific disease cognition and psychological behavioral responses. Its own adjustment is employed to promote patient adaptability and disease outcome, which is more significant than treating the disease itself (Rajabi et al, 2018; Völker et al, 2020)
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