Objective The aim of this study was to investigate the value of Artificial Neural Networks (ANNs) in predicting the occurrence of Venous Thromboembolism (VTE) in patients with Obstructive Sleep Apnea (OSA), and to compare it with traditional Logistic regression models to assess its predictive efficacy, providing theoretical basis for the prediction of VTE risk in OSA patients. Methods A retrospective analysis was conducted on patients diagnosed with OSA and hospitalized in the Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Kunming Medical University, from January 2018 to August 2023. Patients were divided into OSA combined with VTE group (n=128) and pure OSA control group (n=680). The dataset was randomly divided into a training set (n=646) and an independent validation set (n=162). The Synthetic Minority Oversampling Technique (SMOTE) was employed to address the issue of data imbalance. Artificial Neural Networks and Logistic regression models were then built on training sets with and without SMOTE. Finally, the performance of each model was evaluated using accuracy, sensitivity, specificity, Youden's index, and Area Under the Receiver Operating Characteristic Curve (AUC). Results  When oversampling was conducted using SMOTE on the training set, both the Artificial Neural Network and Logistic regression models showed improved AUC. The Artificial Neural Network model with SMOTE performed the best with an AUC value of 0.935 (95% CI: 0.898-0.961), achieving an accuracy of 90.15%, specificity of 87.32%, sensitivity of 93.44%, and Youden's index of 0.808 at the optimal cutoff point. The Logistic regression model with SMOTE yielded an AUC value of 0.817 (95% CI: 0.765-0.861), with an accuracy of 77.27%, specificity of 83.80%, sensitivity of 69.67%, and Youden's index of 0.535. The difference in AUC between the Artificial Neural Network model and Logistic regression model was statistically significant after employing SMOTE (P<0.05). Conclusions  The Artificial Neural Network model demonstrates high effectiveness in predicting VTE formation in OSA patients, particularly with the further improvement in predictive performance when utilizing SMOTE oversampling technique, rendering it more accurate and stable compared to the traditional Logistic regression model.

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