With increasing life expectancy, the elderly population is growing. Hip fractures significantly increase morbidity and mortality, particularly within the first year, among elderly patients. Managing anesthesia in these elderly patients, who often have multiple comorbidities, is challenging. Identifying perioperative factors that can reduce mortality will benefit the perioperative management of these patients.

The aim of this study is to develop and validate a machine learning based model to predict the length of hospital stay for hip fracture patients after PACU. Different machine learning algorithms such as R language Gradient Boosting, Random Forest, Artificial Neural Networks and Logistic Regression will be used in the study and the best performing model will be determined. In addition, the prediction mechanism of the model will be examined with SHAP analysis and its applicability in clinical decision processes will be evaluated. Thus, by predicting the length of hospital stay, clinicians will be enabled to manage patient care processes more effectively.

Inclusion Criteria:

• Patients who underwent hip fracture surgery at our institution between 2017 and 2024
• Patients aged 65 years or older
• Patients with hip fractures resulting from a low-energy trauma (simple fall from standing height)

Exclusion Criteria:

• Patients with pathological hip fractures due to malignancy
• Cancer patients with multiple organ metastases
• Patients who underwent revision hip fracture surgery

Patient data will be accessed retrospectively via the hospital record system. Consistent with the literature, in elderly patients with hip fractures, risk factors for mortality will be assessed preoperatively and postoperatively as well as postoperative complications Patients with and without mortality will be examined in two separate subgroups. All studies for machine learning classification will be conducted at the Artificial Intelligence and Simulation Systems Research and Development Laboratory at Kocaeli University's Faculty of Engineering and will be supervised by a faculty member specializing in artificial intelligence and machine learning.