Identification of Mental Health for Generation Z Using Machine Learning Algorithm

Abstract

Mental health issues such as stress, anxiety, and trauma have become significant challenges, particularly among Generation Z. The lack of effective early detection tools has hindered efforts to address these problems promptly and accurately. This study aims to develop a machine learning-based classification model to detect potential mental health conditions using standardized psychological instruments: DASS-21, STAI, and ACE. Data were collected from 733 youths aged 17–24, of whom 212 exhibited signs of risk. After cleaning and preprocessing, 58 features were retained from the initial 92. Several machine learning models such as Logistic Regression, Support Vector Machine (SVM), and Random Forest were evaluated using class balancing techniques including SMOTE and class weighting. Evaluation metrics are included accuracy, recall, precision, F1-score, and ROC AUC. Logistic regression achieved the highest performance, with 94% accuracy, 100% recall, 82% precision, and an F1-score of 0.90. The ROC AUC reached 99.5%, indicating excellent discriminative ability. This research highlights the effectiveness of machine learning for early detection of mental health conditions and supports its integration into scalable, technology-based mental health screening tools, particularly for at-risk youth populations.