№ files_lp_4_process_2_89841
Year: 2026
Region: Not specified
Subject: Mental health, machine learning, psychiatric disorders
Document type: Supplementary material
Institution: Not specified
Authors: Not specified
Target audience: Researchers in psychiatry and computational modeling
Data type: Socio-demographic and experimental data
Analysis methods: ANOVA, LR, RF, XGB, MLP, stacking models, SHAP
Conditions: Major Depressive Disorder (MDD), Schizophrenia (SCZ), Healthy Controls (HC)
Variables analyzed: Reward components, reward consequences, reward magnitudes
Performance metrics: Accuracy (ACC), Area Under Curve (AUC), feature importance
Subgroup analyses: Symptom severity, medication status
Figures and tables: S1–S26, learning curves, confusion matrices, SHAP plots
Context: Supplementary research tables and figures detailing socio-demographic analyses, feature definitions, model performance, and feature importance for psychiatric disorder classification.
Price: 8 / 10 USD
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