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

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...

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Autores principales:	Yulong Li, Baojin Wu, Xiujun Li, Qin Zhou, Xin Yang, Yufei Li
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	meta heuristic algorithm androgenetic alopecia depressive disorder psychological intervention machine learning and fuzzy K-means clustering Genetics QH426-470
Acceso en línea:	https://doaj.org/article/864e625c5e204e078a93beb9b22c8ffb
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spelling	oai:doaj.org-article:864e625c5e204e078a93beb9b22c8ffb2021-11-12T05:29:51ZResearch on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering1664-802110.3389/fgene.2021.751791https://doaj.org/article/864e625c5e204e078a93beb9b22c8ffb2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.751791/fullhttps://doaj.org/toc/1664-8021Under 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.Yulong LiBaojin WuXiujun LiQin ZhouXin YangYufei LiFrontiers Media S.A.articlemeta heuristic algorithmandrogenetic alopeciadepressive disorderpsychological interventionmachine learning and fuzzy K-means clusteringGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	meta heuristic algorithm androgenetic alopecia depressive disorder psychological intervention machine learning and fuzzy K-means clustering Genetics QH426-470
spellingShingle	meta heuristic algorithm androgenetic alopecia depressive disorder psychological intervention machine learning and fuzzy K-means clustering Genetics QH426-470 Yulong Li Baojin Wu Xiujun Li Qin Zhou Xin Yang Yufei Li Research on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering
description	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.
format	article
author	Yulong Li Baojin Wu Xiujun Li Qin Zhou Xin Yang Yufei Li
author_facet	Yulong Li Baojin Wu Xiujun Li Qin Zhou Xin Yang Yufei Li
author_sort	Yulong Li
title	Research on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering
title_short	Research on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering
title_full	Research on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering
title_fullStr	Research on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering
title_full_unstemmed	Research on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering
title_sort	research on mental stress recognition of depressive disorders in patients with androgenic alopecia based on machine learning and fuzzy k-means clustering
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/864e625c5e204e078a93beb9b22c8ffb
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Research on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering

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