Using machine learning for accurate prediction: unlocking a new chapter in risk assessment for postoperative pneumonia in osteosarcoma

Abstract Objective This study aimed to develop and validate risk prediction models for postoperative pneumonia (POP) in osteosarcoma (OS) patients using multiple machine learning (ML) algorithms, ultimately selecting the model with optimal predictive performance for risk assessment. Methods A retrospective analysis was performed on the clinical data from 290 patients with OS who underwent surgical intervention. The dataset was preprocessed and feature-selected using statistical methods such as t