# Explainable machine learning-based prediction of early and mid-term postoperative complications in adolescent tibial fractures

## TL;DR

This study creates an explainable machine learning model to predict postoperative complications in adolescents with tibial fractures, using coagulation and clinical data.

## Contribution

The novel contribution is an explainable AutoML model optimized with IHSO for predicting postoperative complications in adolescent tibial fractures.

## Key findings

- The IHSO-optimized model outperformed controls in 91.67% of CEC2022 benchmark functions.
- Age, operative duration, and coagulation-inflammation networks were key predictors of complications.
- The model achieved high ROC-AUC scores (0.9667 in training, 0.9247 in testing).

## Abstract

Adolescent tibial fractures commonly lead to postoperative complications. Conventional coagulation markers (PT/APTT/FIB) lack combinatorial risk assessment. We developed an explainable ML model integrating coagulation and clinical features to predict adverse events.

A retrospective cohort of 624 surgical patients (13–18 years) was analyzed. AutoML with Improved Harmony Search Optimization (IHSO) processed features: age, fracture classification, surgery duration, blood loss, and 24 h-postoperative labs (coagulation triad/D-dimer/CRP). Primary outcome: 90-day composite adverse events (DVT/infection/early callus formation disorder/reoperation). SHAP explained predictions.

Baseline characteristics were balanced between training and test sets (P > 0.05). The IHSO-optimized algorithm outperformed controls in 91.67% of CEC2022 benchmark functions. AutoML model performance significantly surpassed conventional methods: training set ROC-AUC: 0.9667, test set ROC-AUC: 0.9247 (PR-AUC: 0.8350). Decision curves demonstrated clinical net benefit across 6%–99% risk thresholds. Key feature importance ranked as: age > operative duration > fibrinogen > fracture classification > APTT > CRP > BMI > D-dimer. SHAP analysis revealed: 1) Increasing age significantly attenuates the risk contribution of surgery duration; 2) FIB >4.0 g/L + elevated CRP indicated coagulation-inflammation cascade; 3) AO-C type fractures carried highest risk.

This AutoML model, validated through explainability techniques, confirms the core predictive value of age, operative duration, and coagulation-inflammation networks for adolescent tibial fracture risk management. Though requiring prospective validation, the three-tier warning system establishes a stepped framework for individualized intervention. Future studies should advance multicenter collaborations integrating dynamic monitoring indicators to optimize clinical applicability.