A Comparative Study of Ensemble Learning Algorithms and Logistic Regression Models for Predicting Postoperative Complications in Orthopedic Surgery Patients
Keywords:
Orthopedic surgery, postoperative complications, machine learning, logistic regression, ensemble learning, risk prediction, Random Forest, Gradient BoostingAbstract
Postoperative complications in orthopedic surgery pose significant risks to patient health and healthcare systems. Accurate prediction of these complications can enhance clinical decision-making and patient outcomes. This study evaluates the performance of ensemble learning algorithms—Random Forest, Gradient Boosting, and AdaBoost—against traditional logistic regression models in predicting postoperative complications using clinical data collected from orthopedic surgery patients. Data preprocessing, model training, and performance evaluation were conducted using standard machine learning pipelines. Results indicate that ensemble models outperform logistic regression in terms of predictive accuracy and sensitivity, highlighting their potential for deployment in clinical risk stratification.
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Copyright (c) 2021 George Papadakis (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
