A Predictive Model for Early Identification of Mental Health Risk among College Students Using Behavioral and Academic Indicators

Mental health issues and concerns, including stress, anxiety, and associated conditions, have become a serious problem in college communities. The consequences of this situation for students’ academic performance, social life, and overall well-being can be severe. At the same time, the ability of educational facilities to predict and respond to the emergence of such a situation are limited. Therefore, the idea of creating a machine learning model that would be able to predict students’ mental health problems was developed. The proposed project would utilize both academic- and behavioural-related features to identify individuals at risk effectively. The results of the work would allow colleges and universities to respond adequately to the situation in advance and reduce the severity of negative consequences for students. For the purpose of the research, the classification models, including Logistic Regression and Random Forest, were trained on the collected data set to identify the most accurate and effective algorithm. The data set used for training contains both academic- and behavioural-related features, including attendance, grades, sleep schedule, number of friends, and other characteristics. The data set was split into testing and training subsets and underwent preliminary processing steps before proceeding to model training. The models of logistic regression and random forest were trained to predict students’ mental health issues based on the selected set of features. As a result of quantitative analysis and evaluation of precision, recall, F1-score, and accuracy, the performance of the two models was compared and analyzed. Based on the results, it can be concluded that both random forest and logistic regression algorithms can be used for mental health predictions as they demonstrate high accuracy and efficiency. Experimental results confirmed that the combination of Behavioral and academic features outperformed the individual set of features. In particular, the best results were achieved by the ensemble methods, among which Random Forest showed the best performance and allowed increasing the detection level of students at risk. This study contributes to addressing the issue by providing an efficient method for early risk detection, which supports the ethical use of information by respecting privacy, informed consents, and responsible implementation. Moreover, the research opens a perspective for improving universities’ counselling services in accordance with the latest trends emphasizing the importance of preventive measures.