Validating machine learning approaches for prediction of donor related complication in microsurgical breast reconstruction: a retrospective cohort study.

ABSTRACT: Autologous reconstruction using abdominal flaps remains the most popular method for breast reconstruction worldwide. We aimed to evaluate a prediction model using machine-learning methods and to determine which factors increase abdominal flap donor site complications with logistic regression. We evaluated the predictive ability of different machine learning packages, reviewing a cohort of breast reconstruction patients who underwent abdominal flaps. We analyzed 13 treatment variables for effects on the abdominal donor site complication rates. To overcome data imbalances, random over sampling example (ROSE) method was used. Data were divided into training and testing sets. Prediction accuracy, sensitivity, specificity, and predictive power (AUC) were measured by applying neuralnet, nnet, an

SUBMITTER: Myung Y 

PROVIDER: S-EPMC7946880 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature