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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Predicting Remission in Schizophrenia Using Machine Learning-Assessing the Impact of Sample Size and Predictor

Fredrik Hieronymus^1,2, Magnus Hieronymus³, Axel Sjöstedt¹

¹Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden.

Acta Psychiatrica Scandinavica

|September 10, 2025

Summary

Machine learning can predict schizophrenia outcomes from small datasets, but only if uninformative predictors are excluded. Careful feature selection is crucial for generalizable results in psychiatric research.

Keywords:

item‐level analysis machine learning schizoaffective disorder schizophrenia supervised learning symptom remission symptom‐level analysis

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Area of Science:

Psychiatry
Machine Learning
Computational Neuroscience

Background:

Machine learning studies risk overfitting and poor generalizability with numerous predictors relative to training cases.
Previous research suggested trial heterogeneity limited schizophrenia outcome prediction.
An alternative explanation is predictor overinclusion causing low generalizability.

Purpose of the Study:

To investigate the impact of predictor inclusion on the generalizability of machine learning models for schizophrenia outcome prediction.
To assess the performance of supervised learning models with varying numbers of training cases and predictor sets.
To determine if predictor overinclusion, rather than heterogeneity, explains poor generalizability in schizophrenia prediction models.

Main Methods:

Utilized Positive and Negative Syndrome Scale (PANSS) item-data, age, sex, and treatment allocation from 18 trials.
Trained five supervised learning models to predict symptom remission after 4 weeks.
Conducted sensitivity analyses with varying training cases and simulated uninformative predictors, including analyses on simulated data.

Main Results:

Better-than-chance predictions (BAC 0.60) were achieved with as few as 384 training cases.
Model performance improved with more training cases (BAC 0.63) and was higher on unseen trials without placebo controls (BAC 0.68).
Including uninformative predictors substantially decreased predictive performance; larger sample sizes may be needed to distinguish weak from uninformative predictors.

Conclusions:

Supervised learning models can predict schizophrenia outcomes from small datasets if uninformative predictors are minimized.
The lack of highly predictive models suggests clinical trial data may lack strong linear predictors for outcomes.
Future machine learning analyses should prioritize identifying weakly predictive features to enhance generalizability.