ML Tool to Identify the Correlation Between Student Mental Factors and Their Performances

The education system plays a vital role in shaping careers, and student mental health has emerged as a critical area of research. This study investigates the correlation between mental health factors and academic performance, aiming to predict students' academic outcomes using machine learning algorithms. Key mental health factors analyzed include gender, pressure, family issues, fear, anxiety, obsession, paranoia, nervousness, injury, interpersonal sensitivity, stage fear, and stress. Using the Eclat algorithm, frequent patterns in mental health data were identified, which were then processed by a Naive Bayes classifier to predict academic performance. Results reveal a significant impact of mental health on academic outcomes, highlighting essential factors influencing student success. A real-time web platform, developed using Microsoft technologies, supports predictive insights, allowing educators and policymakers to identify at-risk students and implement timely interventions.