Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study

• DOI: 10.1111/cns.13991
• Publication Date: 2022-10-11T07:09:10Z
• Authors: Yu‐xiang Song; Xiao‐dong Yang; Yun‐gen Luo; Chun‐lei Ouyang; Yao Yu; Yu‐long Ma; Hao Li; Jing‐sheng Lou; Yan‐hong Liu; Yi‐qiang Chen; Jiang‐bei Cao; Wei‐dong Mi
• Keywords: AdaBoost

Abstract Aims To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) elderly patients. Method This was a retrospective study perioperative medical data from patients undergoing non‐cardiac non‐neurology surgery over 65 years old January 2014 to August 2019. Forty‐six variables were used predict POD. A traditional five models (Random Forest, GBM, AdaBoost, XGBoost, stacking ensemble model) compared by area under receiver operating characteristic curve (AUC‐ROC), sensitivity, specificity, precision. Results In total, 29,756 enrolled, incidence POD 3.22% after variable screening. AUCs 0.783 (0.765–0.8) for method, 0.78 random forest, 0.76 0.74 0.73 0.77 model. The respective sensitivities 6 aforementioned 74.2%, 72.2%, 76.8%, 63.6%, 71.6%, 67.4%. specificities 70.7%, 99.8%, 96.5%, 98.8%, 96.1%. precision values 7.8%, 52.3%, 55.6%, 57%, 54.5%, 56.4%. Conclusions optimal application model could provide quick convenient risk identification help improve management surgical because its better fewer variables, easier interpretability than