Early Mental Health Detection with Machine Learning : A Practical Approach to Model Development and Implementation - Hermawan, Latius; Syakurah, Rizma Adlia; Meilinda, Meilinda; Stiawan, Deris; Negara, Edi Surya; Ramayanti, Indri; Fahmi, Muhammad; Rizqie, Muhammad Qurhanul; Hermanto, Dedy (IAES Indonesian Section, 2025)
Academic pressures, lifestyle changes, and socio-economic factors significantly impact mental health, a critical determinant of academic success and well-being. Early detection and intervention are crucial to mitigate severe outcomes like academic underperformance and suicidal tendencies. Leveraging tools like the DASS-42, this study examines mental health patterns using Support Vector Machine (SVM) models, achieving accuracies of 88% for depression, 71% for stress, and 57% for anxiety. While the model excels in identifying