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Predictive Model Development to Identify Failed Healing in Patients after Non-Union Fracture Surgery
To demonstrate the effectiveness of ML in identifying candidates at risk of failed non-union healing, we applied three ML models (logistic regression, support vector machine, and XGBoost) to the clinical dataset TRUFFLE, which includes 797 patients with long bone non-union.
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