<![CDATA[This study aims to predict adolescents' mood based on physical activity patterns using machine learningalgorithms. Physical activity is often closely correlated with emotional states, making this approachpotentially valuable in providing new insights to support mental health. Data were collected from 50university students using wearable devices over 90 days, including parameters such as step count, activityduration, intensity, and sleep patterns. Mood data were obtained using the BRUMs scale, which had beenadapted and validated. Decision Tree and Support Vector Machine (SVM) algorithms were employed todevelop the predictive model, with performance evaluated based on accuracy, precision, recall, and F1-scoremetrics. The results showed that the Decision Tree outperformed SVM, achieving an accuracy of 98.44%compared to 97.45%. Decision Tree also demonstrated advantages in model interpretability andcomputational efficiency, which are crucial for implementing real-time predictive applications. This studyconcludes that the Decision Tree algorithm is a more effective approach for mood prediction based onphysical activity patterns in adolescents. These findings are expected to form the foundation for developingmental health support systems based on wearable technology.]]>
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