Prediction of Anxiety Disorders using Machine Learning Techniques

Anika Kapoor, Shivani Goel
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引用次数: 1

Abstract

Anxiety disorders have seen an elevating number since the Covid-19 pandemic. This paper aims at identifying more about the various anxiety disorders using machine learning Techniques. Further, symptoms of the types of anxiety disorders: Generalized Anxiety Disorder, Panic Disorder, Post-Traumatic Stress Disorder, Obsessive-Compulsive Disorder and Social Anxiety Disorder are also discussed. The datasets used in the paper are collected by researchers from hospitals/organizations/educational institutions mainly through questionnaires and surveys. Some of the many Machine Learning techniques used for prediction of these anxiety disorders include Random Forest, Linear Regression, Support Vector Machine among others. Lastly, the performance metric for the techniques is presented here and henceforth, the result is drawn from this available data followed by the conclusion.
使用机器学习技术预测焦虑症
自2019冠状病毒病大流行以来,焦虑症的人数不断上升。本文旨在利用机器学习技术识别更多关于各种焦虑症的信息。此外,焦虑症的症状类型:广泛性焦虑症,恐慌症,创伤后应激障碍,强迫症和社交焦虑症也进行了讨论。本文使用的数据集主要由医院/组织/教育机构的研究人员通过问卷调查的方式收集。用于预测这些焦虑症的许多机器学习技术包括随机森林、线性回归、支持向量机等。最后,本文给出了这些技术的性能指标,此后,从这些可用数据中得出结果,然后得出结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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