Early-onset psychosis presents diagnostic challenges , typically requiring specialized tertiary care with multidisciplinary evaluation. This case-control study investigated whether machine learning could integrate multiple diagnostic modalities to create an objective diagnostic framework for early-onset psychosis.
We recruited 45 patients with early-onset psychosis and 34 healthy controls from a tertiary referral centre. Participants underwent comprehensive assessment including serum protein biomarker analysis , neuropsychometric testing. Four machine learning algorithms were trained on five feature combinations using nested cross-validation with hyperparameter optimization. XGBoost demonstrated superior performance, achieving optimal classification with the complete multimodal dataset. Machine learning effectively integrated neuropsychometric and protein biomarker data for high-accuracy early-onset psychosis classification.
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