Research on Mental Stress Recognition of Depressive Disorders in Patients with Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering

November 2021 in “ Frontiers in Genetics ”

Yulong Li, Bao-jin Wu, Xiujun Li, Wenjun Liu, Xin Yang, Yufei Li

androgenetic alopecia AGA depressive disorders machine learning fuzzy K-means clustering psychological nursing FAW-FS algorithm EEG signal depression anxiety treatment compliance hair loss mental health psychological care electroencephalogram

TLDR The FAW-FS algorithm improves depression recognition, and psychological interventions help AGA patients' mental health.

The study explored a new algorithm for recognizing depressive disorders in patients with androgenetic alopecia (AGA) using machine learning and fuzzy K-means clustering. Involving 54 AGA patients split into control and intervention groups, the intervention group received comprehensive psychological nursing. The FAW-FS algorithm showed higher classification accuracy (80.87%-83.07%) and improved EEG signal clarity. Post-intervention, the intervention group had significant reductions in depression and anxiety scores and better treatment compliance (88.89%) compared to the control group. The study concludes that the FAW-FS algorithm enhances depression recognition and that psychological interventions significantly aid in the mental health rehabilitation of AGA patients.

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