Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble

Objective: To propose an ensemble model of artificial neural networks (ANNs) to predict cardiorespiratory morbidity after pulmonary resection for non-small cell lung cancer (NSCLC). Methods: Prospective clinical study was based on 489 NSCLC operated cases. An artificial neural network ensemble was developed using a training set of 348 patients undergoing lung resection between 1994 and 1999. Predictive variables used were: sex of the patient, age, body mass index, ischemic heart disease, cardiac arrhythmia, diabetes mellitus, induction chemotherapy, extent of resection, chest wall resection, perioperative blood transfusion, tumour staging, forced expiratory volume in 1 s percent (FEV1%), and predicted postoperative FEV1% (ppoFEV(1)%). The analysed outcome was the occurrence of postoperative cardio-respiratory complications prospectively recorded and codified. The artificial neural network ensemble consists of 100 backpropagation networks combined via a simple averaging method. The probabilities of complication calculated by ensemble model were obtained to the actual occurrence of complications in 141 cases operated on between January 2000 and December 2001 and a receiver operating characteristic (ROC) curve for this method was constructed. Results: The prevalence of cardio-respiratory morbidity was 0.25 in the training and 0.30 in the validation series. The accuracy for morbidity prediction (area under the ROC curve) was 0.98 by the ensemble model. Conclusion: In this series an artificial neural network ensemble offered a high performance to predict postoperative cardio-respiratory morbidity. (C) 2003 Elsevier Science B.V. All rights reserved.