Predicting Mental Health Disorders Using Machine Learning and Symptom-Based Behavioral Features - Marwan Ramdhany Edy; Nur Fakhrunnisaa (Information Technology Education Journal, 2025)
Mental health disorders, such as anxiety and depression, represent significant challenges in today's fast-paced world. Traditional methods of diagnosing and managing these conditions often fall short, leading to a growing interest in leveraging technology for better mental health outcomes. This paper explored the potential of using machine learning algorithms applied to psychological symptom data to enhance the prognosis of mental disorders. A dataset comprising 120 psychology patients with 17 essential symptoms used to diagnose Mania Bipolar Disorder, Depressive Bipolar Disorder, Major Depressive Disorder, and non-disordered participants was analyzed. The study evaluated the performance of various machine learning models, including RandomForestClassifier, ExtraTreesClassifier, XGBClassifier, LGBMClassifier, and CatBoostClassifier, using metrics such as precision and recall. ExtraTreesClassifier achieved the highest test accuracy of 0.8889, followed by RandomForestClassifier and LGBMClassifier with 0.8611, CatBoostClassifier with 0.8333, and XGBClassifier with 0.7222. Key predictors of mental health outcomes included "Mood_Swing_YES," "Optimism," and "Sexual_Activity." These findings suggest that integrating psychological symptom data with machine learning significantly improves the accuracy of mental health disorder prognosis, offering a promising avenue for more proactive and personalized mental health care. This research demonstrates the practical applicability of machine learning for personalized early detection and intervention in mental healthcare.