<![CDATA[Students mental health is a key factor in their academic and social development. However, the patterns and factors that influence mental health in college students are still not fully understood. This study utilizes machine learning-based clustering and classification techniques to identify hidden patterns in college students’ mental health data, focusing on social and demographic factors. Using the K-Means algorithm for clustering and Random Forest for classification, we group college students based on their mental health conditions and analyze the associations between variables such as age, marital status, anxiety, and medical history. The process begins with data exploration, followed by data cleaning and feature transformation to ensure optimal input quality. In the clustering stage, we find three main groups of college students with different mental health patterns, which are then used as the basis for a classification model. A Random Forest model is built to predict potential mental disorders, such as depression and anxiety, by identifying the features that have the most influence on the prediction results. The model evaluation shows significant performance with adequate accuracy, where the importance of social factors such as marital status and history of visits to medical professionals is clearly revealed. The results of this study not only offer important insights into students’ mental health patterns, but also provide recommendations for university policies in creating an environment that supports students’ mental well-being. This combined approach of clustering and classification opens up new opportunities in the application of machine learning for more precise and data-driven mental health analysis.]]>
