Comparative performance of logistic regression, multilayer perceptron and decision tree models for predicting...

Surgical pressure injuries (SPIs) are a significant patient safety risk due to prolonged immobility and tissue hypoperfusion under general anaesthesia. Existing risk assessment tools lack real-time predictive capabilities. This study developed and validated a machine-learning model for SPI prediction and clinical integration.

This retrospective cohort study analysed electronic health records from 931 surgical inpatients under general anaesthesia between January 2016 and December 2021. SPI cases were identified using ICD-10 codes with 1:1 matching by medical specialty. Data preprocessing included imputation, normalisation and outlier removal. Logistic regression (LR), multilayer perceptron (MLP) and decision tree (DT) models were developed and validated via cross-validation. Model performance was assessed using area under the curve (AUC), accuracy, precision, recall and F1 score.

Significant SPI predictors included the Charlson Comorbidity Index (p<0.001), number of medication types (p=0.001) and body mass index (p<0.001). The MLP outperformed LR (AUC=0.707) and DT (AUC=0.717), achieving the highest AUC (0.836), accuracy (0.773), precision (0.812), recall (0.688) and F1 score (0.745).

The MLP model effectively identified key SPI risk factors, outperforming LR and DT by capturing non-linear relationships. Its integration into clinical workflows may enhance perioperative risk management through early detection and targeted interventions.

Machine learning integration can improve early SPI detection and personalised prevention. The MLP model demonstrated the highest potential for real-time SPI risk stratification. Future research should validate this model across diverse surgical populations and develop scalable strategies for clinical implementation.