Prediction of Mental Health Problem Using Annual Student Health Survey: A Machine Learning Approach (Preprint)

Machine learning-enabled mental health risk prediction for youths with stressful life events: A modelling study

BACKGROUND

Youths face significant mental health challenges exacerbated by stressful life events, particularly in the context of the COVID-19 pandemic. Immature coping strategies can worsen mental health outcomes.

METHODS

This study utilised a two-wave cross-sectional survey design with data collected from Chinese youth aged 14-25 years. Wave 1 (N = 3038) and Wave 2 (N = 539) datasets were used for model development and external validation, respectively. Twenty-five features, encompassing dimensions related to demographic information, stressful life events, social support, coping strategies, and emotional intelligence, were input into the model to predict the mental health status of youth, which was considered their coping outcome. Shapley additive explanation (SHAP) was used to determine the importance of each risk factor in the feature selection. The intersection of top 10 features identified by random forest and XGBoost were considered the most influential predictors of mental health during the feature selection process, and was then taken as the final set of features for model development. Machine learning models, including logistic regression, AdaBoost, and a backpropagation neural network (BPNN), were trained to predict the outcomes. The optimum model was selected according to the performance in both internal and external validation.

RESULTS

This study identified six key features that were significantly associated with mental health outcomes: punishment, adaptation issues, self-regulation of emotions, learning pressure, use of social support, and recognition of others' emotions. The BPNN model, optimized through feature selection methods like SHAP, demonstrated superior performance in internal validation (C-index [95 % CI] = 0.9120 [0.9111, 0.9129], F-score [95 % CI] = 0.8861 [0.8853, 0.8869]). Additionally, external validation showed the model had strong discrimination (C-index = 0.9749, F-score = 0.8442) and calibration (Brier score = 0.029) capabilities.

LIMITATIONS

Although the clinical prediction model performed well, the study it still limited by self-reported data and representativeness of samples. Causal relationships need to be established to interpret the coping mechanism from multiple perspectives. Also, the limited data on minority groups may lead to algorithmic unfairness.

CONCLUSIONS

Machine learning models effectively identified and predicted mental health outcomes among youths, with the SHAP+BPNN model showing promising clinical applicability. These findings emphasise the importance and effectiveness of targeted interventions with the help of clinical prediction model.

Examining the interplay between mental health indicators and quality of life measures among first-year law students: a cross-sectional study

Introduction

This research explores the intricate relationships between mental health indicators (depression, stress, and anxiety) and various dimensions of quality of life among first-year law students. The study aims to understand how affective valence, mood states, physical activity, body image perception, and social relations influence mental health outcomes.

Methods

Data were collected from 75 first-year law students (46 females, 29 males), a group predominantly composed of young adults with limited financial means, living in various housing situations, primarily within urban environments, and generally reporting low levels of physical activity. Standardized questionnaires were used to assess mental health and quality of life, including the Depression, Anxiety, and Stress Scale-21 Items (DASS-21), Feeling Scale (FS), Exercise-Induced Feeling Inventory (EIFI), Modified Baecke Physical Activity Questionnaire (MBPAQ), World Health Organization Quality of Life-BREF (WHOQOL-BREF), and Contour Drawing Rating Scale (CDRS). Descriptive statistics, Pearson correlation, and regression analysis were employed to analyze the data.

Results

The analysis revealed significant correlations between depression (mean = 5.97, SD = 4.21), stress (mean = 7.81, SD = 4.80), and anxiety (mean = 6.17, SD = 4.58) with affective valence (p < 0.05), mood states (p < 0.05), physical activity (p < 0.05), body image perception (p < 0.05), and social relations quality (p < 0.05). Additionally, mood states (mean = 20.73, SD = 10.60), physical activity (mean = 8.43, SD = 1.35), body image perception (mean = 4.21, SD = 1.91), and social relations quality (mean = 12.46, SD = 2.33) were identified as significant predictors of mental health outcomes (p < 0.05).

Conclusions

These findings underscore the complex interplay between mental health indicators and various dimensions of quality of life, emphasizing the necessity for a comprehensive approach to mental health care. By identifying these predictors, we have gained a clearer understanding of the factors that impact mental health in this specific population. The insights gained highlight the value of interventions aimed at improving mood, increasing physical activity, enhancing body image, and strengthening social connections. These targeted strategies could effectively address mental health issues and promote well-being among law students. Future research should further investigate these relationships and develop tailored interventions to better support students' mental health. This study contributes to understanding the complex interplay between mental health and quality of life, offering a foundation for both practical interventions and future research.

An artificial intelligence tool to assess the risk of severe mental distress among college students in terms of demographics, eating habits, lifestyles, and sport habits: an externally validated study using machine learning

BACKGROUND

Precisely estimating the probability of mental health challenges among college students is pivotal for facilitating timely intervention and preventative measures. However, to date, no specific artificial intelligence (AI) models have been reported to effectively forecast severe mental distress. This study aimed to develop and validate an advanced AI tool for predicting the likelihood of severe mental distress in college students.

METHODS

A total of 2088 college students from five universities were enrolled in this study. Participants were randomly divided into a training group (80%) and a validation group (20%). Various machine learning models, including logistic regression (LR), extreme gradient boosting machine (eXGBM), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were employed and trained in this study. Model performance was evaluated using 11 metrics, and the highest scoring model was selected. In addition, external validation was conducted on 751 participants from three universities. The AI tool was then deployed as a web-based AI application.

RESULTS

Among the models developed, the eXGBM model achieved the highest area under the curve (AUC) value of 0.932 (95% CI: 0.911-0.949), closely followed by RF with an AUC of 0.927 (95% CI: 0.905-0.943). The eXGBM model demonstrated superior performance in accuracy (0.850), precision (0.824), recall (0.890), specificity (0.810), F1 score (0.856), Brier score (0.103), log loss (0.326), and discrimination slope (0.598). The eXGBM model also received the highest score of 60 based on the evaluation scoring system, while RF achieved a score of 49. The scores of LR, DT, and SVM were only 19, 32, and 36, respectively. External validation yielded an impressive AUC value of 0.918.

CONCLUSIONS

The AI tool demonstrates promising predictive performance for identifying college students at risk of severe mental distress. It has the potential to guide intervention strategies and support early identification and preventive measures.

Exploring the relationship between response time sequence in scale answering process and severity of insomnia: A machine learning approach

Utilizing computer-based scales for cognitive and psychological evaluations allows for the collection of objective data, such as response time. This cross-sectional study investigates the significance of response time data in cognitive and psychological measures, with a specific focus on its role in evaluating sleep quality through the Insomnia Severity Index (ISI) scale. A mobile application was designed to administer scale tests and collect response time data from 2729 participants. We explored the relationship between symptom severity and response time. A machine learning model was developed to predict the presence of insomnia symptoms in participants using response time data. The result revealed a statistically significant difference (p < 0.01) in the total response time between participants with or without insomnia symptom. Furthermore, a strong correlation was observed between the severity of specific insomnia aspects and the response times at the individual questions level. The machine learning model demonstrated a high predictive Area Under the ROC Curve (AUROC) of 0.824 in predicting insomnia symptoms based on response time data. These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures.

Anticipating interpersonal sensitivity: A predictive model for early intervention in psychological disorders in college students

Psychological disorders, notably social anxiety and depression, exert detrimental effects on university students, impeding academic achievement and overall development. Timely identification of interpersonal sensitivity becomes imperative to implement targeted support and interventions. This study selected 958 freshmen from higher education institutions in Zhejiang province as the research sample. Utilizing the runge-kutta search and elite levy spreading enhanced moth-flame optimization (MFO) in conjunction with the kernel extreme learning machine (KELM), we propose an efficient intelligent prediction model, namely bREMFO-KELM, for predicting the interpersonal sensitivity of college students. IEEE CEC 2017 benchmark functions and the interpersonal sensitivity dataset were employed as the basis for detailed comparisons with peer-reviewed studies and well-known machine learning models. The experimental results demonstrate the outstanding performance of the bREMFO-KELM model in predicting the sensitivity of interpersonal relationships in college students, achieving an impressive accuracy rate of 97.186%. In-depth analysis reveals that the prediction of interpersonal sensitivity in college students is closely associated with multiple features, including easily hurt in relationships, shy and uneasy with the opposite sex, feeling inferior to others, discomfort when observed or discussed, and blame and criticize others. These features are not only crucial for the accuracy of the prediction model but also provide valuable information for a deeper understanding of the sensitivity of college students' interpersonal relationships. In conclusion, the bREMFO-KELM model excels not only in performance but also possesses a high degree of interpretability, providing robust support for predicting the sensitivity of interpersonal relationships in college students.

Evaluating Machine Learning Stability in Predicting Depression and Anxiety Amidst Subjective Response Errors

Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) pose significant burdens on individuals and society, necessitating accurate prediction methods. Machine learning (ML) algorithms utilizing electronic health records and survey data offer promising tools for forecasting these conditions. However, potential bias and inaccuracies inherent in subjective survey responses can undermine the precision of such predictions. This research investigates the reliability of five prominent ML algorithms-a Convolutional Neural Network (CNN), Random Forest, XGBoost, Logistic Regression, and Naive Bayes-in predicting MDD and GAD. A dataset rich in biomedical, demographic, and self-reported survey information is used to assess the algorithms' performance under different levels of subjective response inaccuracies. These inaccuracies simulate scenarios with potential memory recall bias and subjective interpretations. While all algorithms demonstrate commendable accuracy with high-quality survey data, their performance diverges significantly when encountering erroneous or biased responses. Notably, the CNN exhibits superior resilience in this context, maintaining performance and even achieving enhanced accuracy, Cohen's kappa score, and positive precision for both MDD and GAD. This highlights the CNN's superior ability to handle data unreliability, making it a potentially advantageous choice for predicting mental health conditions based on self-reported data. These findings underscore the critical importance of algorithmic resilience in mental health prediction, particularly when relying on subjective data. They emphasize the need for careful algorithm selection in such contexts, with the CNN emerging as a promising candidate due to its robustness and improved performance under data uncertainties.