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Schizophrenia, a term introduced by Swiss psychiatrist Eugen Bleuler in 1911, describes a severe psychological disorder marked by profound disruptions in attention, thought processes, language, emotion, and interpersonal relationships. The core feature of schizophrenia is psychosis — a state characterized by a fundamental detachment from reality. This disconnection manifests through distorted logic, impaired perception, and atypical behavior, severely affecting the lives of those...
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Schizophrenia, a severe psychiatric disorder, arises from a complex interplay of biological factors, including genetic predisposition, structural brain abnormalities, neurotransmitter dysregulation, and developmental irregularities. These factors collectively contribute to the onset and progression of the disorder, which typically manifests in late adolescence or early adulthood.
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A clinical prediction model for schizophrenia based on machine learning algorithms.

Weifeng Jin1, Shuzi Chen1, Qiong Gao1

  • 1Department of Medical Laboratory, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Frontiers in Medicine
|January 21, 2026
PubMed
Summary

This study developed an auxiliary diagnostic tool for schizophrenia using machine learning and routine blood tests. The logistic regression model shows promise as a diagnostic aid for early schizophrenia detection.

Keywords:
biomarkersmachine learningnomogrampredictive modelschizophrenia

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Area of Science:

  • Biomedical Informatics
  • Computational Psychiatry
  • Clinical Diagnostics

Background:

  • Schizophrenia diagnosis can be challenging, necessitating improved auxiliary tools.
  • Routine blood tests offer a potential source of biomarkers for schizophrenia detection.

Purpose of the Study:

  • To develop and validate a machine learning-based diagnostic tool for first-episode schizophrenia.
  • To identify key peripheral blood biochemical indicators and demographic data predictive of schizophrenia.

Main Methods:

  • Retrospective analysis of blood biochemical indicators and demographic data from 180 first-episode schizophrenia patients and 214 controls.
  • Feature selection using Univariate logistic regression, Boruta, and LASSO algorithms.
  • Development and evaluation of seven machine learning models, including Random Forest and logistic regression, with performance metrics like AUC, sensitivity, and specificity.

Main Results:

  • Arg, TP, ALP, HDL, UA, and LDL were identified as significant predictors.
  • The Random Forest model achieved an AUC of 1.00 (training) and 0.877 (validation).
  • A multivariate logistic regression model was selected for its interpretability and robustness, with nomograms constructed for clinical use.

Conclusions:

  • An auxiliary diagnostic tool for schizophrenia was successfully established using machine learning and routine blood indicators.
  • The developed logistic regression model demonstrates good performance and utility as a diagnostic aid for schizophrenia.