Development and validation of a machine learning-based nomogram for preoperative prediction of laparoscopic surgical difficulty in gallstone patients.

Background: Preoperative prediction of laparoscopic surgical difficulty in gallstone patients is crucial for improving surgical outcomes. This study aimed to develop and validate a nomogram based on advanced machine learning algorithms, incorporating key clinical and systemic inflammatory response indicators, such as the C-reactive protein to albumin ratio (CAR). Methods: A retrospective analysis was conducted on 362 eligible patients who underwent laparoscopic cholecystectomy (LC) for gallstones between 2013 and 2019. A total of 420 patients were initially identified, with 58 excluded based on predefined criteria such as age and incomplete records. The remaining patients were divided into a training set (n=253) and a validation set (n=109). The development of the nomogram involved multiple analytical techniques, including machine learning methods such as least absolute shrinkage and selection operator (LASSO) regression, decision tree analysis, and support vector machine (SVM) models, along with traditional statistical methods like univariate and multivariate logistic regression. Significant predictors, including CAR, white blood cell count (WBC), and gallbladder wall thickness, were integrated into the final predictive model. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis and calibration plots. Results: The machine learning-based model demonstrated strong predictive capability, with an area under the curve (AUC) of 0.774 in the training set and 0.863 in the validation set. Calibration plots showed good agreement between predicted and actual outcomes, with mean absolute errors of 0.035 and 0.05 for the training and validation sets, respectively. Conclusions: This study demonstrates the utility of applying machine learning algorithms to develop a robust nomogram for preoperative prediction of laparoscopic surgical difficulty. By integrating key clinical variables and systemic inflammatory markers, the model provides an effective tool for improving surgical planning and enhancing patient outcomes.