Construction of an automated machine learning-based predictive model for postoperative pulmonary complications risk in non-small cell lung cancer patients undergoing thoracoscopic surgery.

ABSTRACT:

Objective

To develop a predictive framework integrating machine learning and clinical parameters for postoperative pulmonary complications (PPCs) in non-small cell lung cancer (NSCLC) patients undergoing video-assisted thoracic surgery (VATS).

Methods

This retrospective study analyzed 286 NSCLC patients (2022-2024), incorporating 13 demographic, metabolic-inflammatory, and surgical variables. An Improved Blood-Sucking Leech Optimizer (IBSLO) enhanced via Cubic mapping and opposition-based learning was developed. Model performance was evaluated using AUC-ROC, F1-score, and decision curve analysis (DCA). SHAP interpretation identified key predictors.

Results

The IBSLO demonstrated significantly superior convergence performance versus original BSLO, ant lion optimizer (A

SUBMITTER: Qiu X 

PROVIDER: S-EPMC12469157 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature