Readmission Risk Prediction After Total Hip Arthroplasty Using Machine Learning and Hyperparameter Optimized with Bayesian Optimization

Machine learning techniques are increasingly used in orthopaedic surgery to assess risks such as length of stay, complications, infections, and mortality, offering an alternative to traditional methods. However, model performance varies depending on private institutional data, and optimizing hyperparameters for better predictions remains a challenge. This study incorporates automatic hyperparameter tuning to improve readmission prediction in orthopaedics using a public medical dataset. Bayesian Optimization was applied to optimize hyperparameters for seven machine learning algorithms- Extreme Gradient Boosting, Stochastic Gradient Boosting, Random Forest, Support Vector Machine, Decision Tree, Neural Network, and Elastic-net Penalized Logistic Regression- predicting readmission risk after Total Hip Arthroplasty (THA). Data from the MIMIC-IV database, including 1,153 THA patients, was used. Model performance was evaluated using Precision, Recall, and AUC-ROC, comparing optimized algorithms to those without hyperparameter tuning from previous studies. The optimized Extreme Gradient Boosting algorithm achieved the highest AUC-ROC of 0.996, while other models also showed improved accuracy, precision, and recall. This research successfully developed and validated optimized machine learning models using Bayesian Optimization, enhancing readmission prediction following THA based on patient demographics and preoperative diagnosis. The results demonstrate superior performance compared to prior studies that either lacked hyperparameter optimization or relied on exhaustive search methods.