Predicting Determinants of Mental Health Status in Malaysian Undergraduate Students Using the Association Rule Mining Technique

Mental health issues pose a major challenge to the well-being and academic success of undergraduate students. Identifying key determinants of mental health is crucial for developing targeted interventions. This study employs association rule mining (ARM) to explore the relationships between educational and personal factors and students' mental health status.

A validated survey was administered to 1,394 undergraduate students (409 males and 985 females) from six Malaysian public universities. The Apriori algorithm was applied with minimum support of 0.1, confidence of 0.7, and lift of > 1 to extract meaningful associations. Data preprocessing included handling missing values, categorical encoding, and outlier detection.

The analysis identified ten key association rules, revealing that female students were more likely to face learning difficulties (support = 0.188, confidence = 0.852, and lift = 1.134) but were also more uncertain about their mental health status. Interestingly, financial problems did not strongly predict mental health issues (support = 0.175, confidence = 0.707, and lift = 1.142).

These findings underscore the need for universities to reduce academic pressures, combat loneliness and isolation, and provide mental health services to support struggling students. With targeted interventions, universities can create healthier learning environments where students can thrive both academically and emotionally.