Comparison of Bayesian, Frequentist and Machine learning models for predicting the two-year mortality of patients diagnosed with squamous cell carcinoma of the oral cavity

Statistical models developed in frequentist and Bayesian context along with machine learning algorithms can encompass the multifactorial effect of the prognostic factors in predicting the outcome. This paper is aimed to compare the effect estimates and predictive performance of Bayesian, frequentist and machine learning algorithm in predicting the two-year mortality of patients diagnosed with squamous cell carcinoma (SCC) of oral cavity. Logistic regression (LR), Binary Discriminant analysis (BDA), Naïve Bayes (NB), Bayesian regression (BLR), K nearest neighbor (KNN), Artificial neural network (ANN) and Random Forest (RF) models were built. The effect estimate of each prognostic factor was estimated and compared by LR and BLR model. 10-fold cross-validation was performed for internal validation of the models. The predictive performance of the models was assessed and compared. BLR model had lower and narrower effect estimates in comparison to LR model. Age and smoking are the biggest prognostic risk factors for SCC whereas surgery had the best response amongst the mode of treatment. Random forest had an AUROC of 0.86 (0.82, 0.90) whereas it was estimated to be 0.77 (0.71, 0.82) for both BLR and LR models. BLR model had better precision in estimating the effect size of prognostic factors and can be an alternative for predicting mortality in patients with SCC of oral cavity. Machine learning classifiers had the best predictive ability as compared to statistical models.