Predicting postoperative complications of head and neck squamous cell carcinoma in elderly patients using random forest algorithm model

Background: Head and Neck Squamous Cell Carcinoma (HNSCC) has a high incidence in elderly patients. The postoperative complications present great challenges within treatment and they're hard for early warning. Methods: Data from 525 patients diagnosed with HNSCC including a training set (n = 513) and an external testing set (n = 12) in our institution between 2006 and 2011 was collected. Variables involved are general demographic characteristics, complications, disease and treatment given. Five data mining algorithms were firstly exploited to construct predictive models in the training set. Subsequently, cross-validation was used to compare the different performance of these models and the best data mining algorithm model was then selected to perform the prediction in an external testing set. Results: Data from 513 patients (age > 60 y) with HNSCC in a training set was included while 44 variables were selected (P < 0.05). Five predictive models were constructed; the model with 44 variables based on the Random Forest algorithm demonstrated the best accuracy (89.084 %) and the best AUC value (0.949). In an external testing set, the accuracy (83.333 %) and the AUC value (0.781) were obtained by using the random forest algorithm model. Conclusions: Data mining should be a promising approach used for elderly patients with HNSCC to predict the probability of postoperative complications. Our results highlighted the potential of computational prediction of postoperative complications in elderly patients with HNSCC by using the random forest algorithm model.