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Interpretable machine learning model to predict aggressive behavior in first-episode schizophrenia
Journal of Psychiatric Research
|October 4, 2025
Summary
Machine learning models can predict aggressive behavior in first-episode schizophrenia patients. The Random Survival Forest model showed high accuracy, identifying impulsivity and income as key risk factors.
Area of Science:
- Psychiatry
- Machine Learning
- Behavioral Science
Background:
- Schizophrenia is a complex mental disorder.
- Aggressive behavior is a significant concern in first-episode schizophrenia patients.
- Predicting aggressive behavior is crucial for timely intervention.
Purpose of the Study:
- To predict the risk of aggressive behavior in first-episode schizophrenia patients.
- To evaluate the performance of time-to-event machine learning models for this prediction.
- To identify key predictors of aggressive behavior in this population.
Main Methods:
- A cohort study design was employed with 216 first-episode schizophrenia patients.
- Patients were followed for 24 months to assess aggressive behavior.
- Three survival models were developed and evaluated using metrics like C-index, time-dependent AUC, and AUPRC. Shapley Additive Explanation (SHAP) was used for feature importance.
Main Results:
- Aggressive behavior was observed in 16.67% of patients during follow-up.
- The Random Survival Forest model exhibited the best performance (C-index=0.79, time-dependent AUC=0.91, AUPRC=0.59).
- High impulsivity, higher average monthly income, larger household size, unemployment, and lower EPQ-L scores were identified as significant predictors.
Conclusions:
- The Random Survival Forest model is effective in predicting aggressive behavior in first-episode schizophrenia.
- Impulsivity and average monthly income are the most significant factors influencing aggressive behavior.
- These findings can aid in developing targeted interventions for at-risk individuals.
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