Enhancing smart healthcare with female students' stress and anxiety detection using machine learning.

IF 2.3 3区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Farhad Lotfi, Amin Lotfi, Matin Lotfi, Artur Bjelica, Zorica Bogdanović
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Abstract

Machine learning (ML) is widely used to predict and detect stress and anxiety. Early detection of stress or anxiety is crucial for clinical pathways to enhance the supportive environment in society, particularly among female students. This study aims to assess and improve the accuracy of detecting stress and anxiety among female students using machine learning algorithms and functions. Three primary features are cigarette smoking, physical activity and grade point average (GPA). The multiple linear regression analysis conducted on 160 datasets obtained from the State-Trait Anxiety Inventory (STAI) at the University of Belgrade was selected. A heat map was utilised to identify the least engaging areas of the model along with most state anxiety factors. Additionally, R-squared (R2), mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE) were employed to assess the errors of the linear regression model for both pre-intervention and post-intervention, focusing on key features related to female students' anxiety. Using the K-Means algorithm, cluster analysis was executed on samples (N = 160) with three key features. The total average anxiety score was 44.39% (out of 80%) and is considered moderate. The heat map indicated a strong relationship between the variables. Overall, the post-intervention stage yielded acceptable results compared to the pre-intervention stage. Two clusters of anxiety among female students were identified, demonstrating that these features can accurately detect anxiety in female students. This research aims to analyse female students' stress and anxiety better using the linear regression algorithm. Additionally, ML functions demonstrated that smoking cigarettes, physical activity and GPA related to the stress and anxiety of female students have reduced errors during anxiety detection.

利用机器学习检测女学生的压力和焦虑,加强智能医疗。
机器学习(ML)被广泛用于预测和检测压力和焦虑。早期发现压力或焦虑对临床途径至关重要,以增强社会的支持性环境,特别是在女学生中。本研究旨在评估并提高利用机器学习算法和功能检测女学生压力和焦虑的准确性。三个主要特征是吸烟、体育活动和平均绩点(GPA)。选取贝尔格莱德大学状态-特质焦虑量表(STAI)的160个数据集进行多元线性回归分析。使用热图来确定模型中最不吸引人的区域以及大多数状态焦虑因素。此外,采用r平方(R2)、平均绝对误差(MAE)、均方误差(MSE)和均方根误差(RMSE)对干预前和干预后的线性回归模型的误差进行评估,重点关注与女学生焦虑相关的关键特征。使用K-Means算法对具有三个关键特征的样本(N = 160)进行聚类分析。总平均焦虑得分为44.39%(总分为80%),属于中度焦虑。热图显示了这些变量之间的密切关系。总体而言,与干预前阶段相比,干预后阶段产生了可接受的结果。在女学生中发现了两个焦虑聚类,表明这些特征可以准确地检测女学生的焦虑。本研究旨在利用线性回归算法更好地分析女大学生的压力和焦虑。此外,ML功能显示吸烟、体育活动和GPA与女学生的压力和焦虑相关,降低了焦虑检测的误差。
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来源期刊
Psychology Health & Medicine
Psychology Health & Medicine PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
6.20
自引率
0.00%
发文量
200
审稿时长
6-12 weeks
期刊介绍: Psychology, Health & Medicine is a multidisciplinary journal highlighting human factors in health. The journal provides a peer reviewed forum to report on issues of psychology and health in practice. This key publication reaches an international audience, highlighting the variation and similarities within different settings and exploring multiple health and illness issues from theoretical, practical and management perspectives. It provides a critical forum to examine the wide range of applied health and illness issues and how they incorporate psychological knowledge, understanding, theory and intervention. The journal reflects the growing recognition of psychosocial issues as they affect health planning, medical care, disease reaction, intervention, quality of life, adjustment adaptation and management. For many years theoretical research was very distant from applied understanding. The emerging movement in health psychology, changes in medical care provision and training, and consumer awareness of health issues all contribute to a growing need for applied research. This journal focuses on practical applications of theory, research and experience and provides a bridge between academic knowledge, illness experience, wellbeing and health care practice.
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