摘要
由一名新闻记者-机器人和机器学习的工作人员新闻编辑每日新闻-关于精神健康疾病和条件的新研究是一篇报道的主题。根据NewsRx记者在英国卡迪夫的新闻报道，研究表明：“近年来，随着越来越多的学生离开祖国接受更好的教育，国际学生的心理健康越来越令人担忧。他们在国外学习时经历了一系列挑战，这些挑战对他们的心理健康产生了影响。”新闻记者从卡迪夫都市大学的研究中获得了一句话，“这些挑战可能包括语言障碍、文化差异、乡愁、经济问题和其他可能严重影响国际学生心理健康的因素。鉴于对影响国际学生心理健康的人口、文化和社会心理变量的研究有限，”由于机器学习算法在这一领域的应用研究很少，本研究旨在分析数据，以了解影响留学生心理健康的地形、文化因素和社会心理因素。本文旨在建立一个基于机器学习的模型来预测在英国留学生的抑郁情绪。该研究利用了通过针对留学生的在线调查问卷收集的原始数据和来自“多元文化环境下学生心理健康和求助行为数据集”的次要数据。本研究以国际学生为研究对象，对国内学生抑郁预测的主要数据进行数据分析，并利用二次数据构建模型，将二次数据分为训练集(70%)和测试集(30%)进行分析，采用逻辑回归、决策树、随机森林和K最近Nei Ghbor四种机器学习模型，评估每种算法的性能。我们考虑了诸如准确性、敏感性、特异性、精密度和AU-ROC曲线等指标。本研究确定了影响留学生心理健康的重要人口统计学变量(如贷款状况、性别、年龄、婚姻状况)和社会心理因素(经济困难、学业压力、家庭和孤独)。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Mental Health Diseases and Conditions is the subject of a report. According to news reporting originat ing in Cardiff, United Kingdom, by NewsRx journalists, research stated, "Interna tional students' mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wi de range of challenges while studying abroad that have an impact on their psycho logical well-being." The news reporters obtained a quote from the research from Cardiff Metropolitan University, "These challenges can include language obstacles, cultural differenc es, homesickness, financial issues and other elements that could severely impact the mental health of international students. Given the limited research on the demographic, cultural, and psychosocial variables that influence international s tudents' mental health, and the scarcity of studies on the use of machine learni ng algorithms in this area, this study aimed to analyse data to understand the d emographic, cultural factors, and psychosocial factors that impact mental health of international students. Additionally, this paper aimed to build a machine le arning-based model for predicting depression among international students in the United Kingdom. This study utilized both primary data gathered through an onlin e survey questionnaire targeted at international students and secondary data was sourced from the ‘A Dataset of Students' Mental Health and Help-Seeking Behavio rs in a Multicultural Environment,' focusing exclusively on international studen t data within this dataset. We conducted data analysis on the primary data and c onstructed models using the secondary data for predicting depression among inter national students. The secondary dataset is divided into training (70% ) and testing (30%) sets for analysis, employing four machine learn ing models: Logistic Regression, Decision Tree, Random Forest, and K Nearest Nei ghbor. To assess each algorithm's performance, we considered metrics such as Acc uracy, Sensitivity, Specificity, Precision and AU-ROC curve. This study identifi es significant demographic variables (e.g., loan status, gender, age, marital st atus) and psychosocial factors (financial difficulties, academic stress, homesic kness, loneliness) contributing to international students' mental health."
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