首页|Cardiff Metropolitan University Reports Findings in Mental Health Diseases and C onditions (Mental health analysis of international students using machine learni ng techniques)
Cardiff Metropolitan University Reports Findings in Mental Health Diseases and C onditions (Mental health analysis of international students using machine learni ng techniques)
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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."
CardiffUnited KingdomEuropeCyborgsEmerging TechnologiesHealth and MedicineInvestment and FinanceMachine Le arningMental Health Diseases and ConditionsPsychosocial
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