A clinical prediction model for schizophrenia based on machine learning algorithms.

Objective: To develop an auxiliary diagnostic tool for schizophrenia based on multiple test variables using different machine learning algorithms. Methods: This retrospective study used routinely collected peripheral blood biochemical indicators, along with demographic data, to develop a diagnostic model for first-episode schizophrenia. A total of 180 patients with first-episode schizophrenia between January and August 2024, and 214 healthy controls as a population undergoing routine medical examinations during the same period. Data on age, gender, and various blood test results were collected. The dataset was divided into a training set (70%; Results: Arg, TP, ALP, HDL, UA, and LDL were ultimately identified as significant predictors through Univariate logistic regression combined with the Boruta and LASSO algorithms. The Random Forest algorithm outperformed other machine learning models, achieving an AUC of 1.00 for the training set and 0.877 for the validation set. However, due to the risk of overfitting, we ultimately selected the multivariate logistic regression model as the final model for our study and constructed nomograms. Conclusion: In this study, an auxiliary diagnostic tool for schizophrenia was established using machine learning algorithms combined with routine blood indicators. The logistic regression model demonstrated good performance and can serve as a diagnostic aid for schizophrenia.