Comparing the predictive performance of a decision tree with logistic regression for oral cavity cancer mortality: A retrospective study

Abstract

Background: A decision tree is a popular predictive modeling technique used to classify observations based on their properties and can also be used for numeric or categorical predictions. A logistic regression model estimates the relationship between a dependent categorical variable and one or more independent variables. Objective: To compare the performances of logistic regression and decision tree models for predicting mortality in patients with oral cavity cancer. Materials and Methods: This was a retrospective study on de-identified records of patients with oral cavity cancer who received treatment at Jawaharlal Institute of Post Graduate Medical Education and Research, a tertiary healthcare hospital in Puducherry, South India, from 2011 to 2017. The models were built by incorporating potential predictive variables such as stage of the cancer, tumor and node categories, and margin status. The performances of the classification models were compared using sensitivity, specificity, predictive accuracy, and by plotting the area under the receiver operating characteristic (ROC) curves. Results: Among the 427 patients who received treatment for oral cavity cancer between 2011 and 2017, we included 275 who had undergone surgery and whose histopathology details were available. There were 180 (65.5%) male patients. The median age was 55 years (range, 26-87). The tumor stage and margin status of the cancer were found to be significant predictors of mortality in a multiple logistic regression model. The predictive accuracies and the areas under the ROC curves of logistic regression and decision tree models were found to be similar. Conclusion: A decision tree method along with other machine learning algorithms could be an innovative alternative to statistical models for the prediction of outcomes when the assumptions of these models fail. The application of these machine learning algorithms can help risk stratify patients with oral cavity cancer and thus aid in their treatment planning.

Keywords

• decision tree
• logistic regression
• mortality
• predictive accuracy