Diagnosis of Head and Neck Cancer in Developing Countries Using a Stacked Ensemble Model

Abstract

Head and neck cancers (HNC) are indicated when cells grow abnormally.  The incidence of HNC is on the increase owing to several factors. There is often late presentation that can result in loss of lives (mortality) especially in Africa due to paucity of specialists. These challenges prompted the development of a stacked ensemble model for diagnosis of HNC to facilitate prompt referral.  The data were collected which consists of 1473 instances with 18 features.   Information Gain was used for selecting important features and three supervised learning algorithms were deployed for the base learners: Decision Tree (C4.5), K-Nearest Neighbors and Naïve Bayes. The predictions of the base learners were combined and passed to meta learners: Logistic Model Tree (LMT). The result showed that Information Gain method with stacked LMTwas 95.11%. It was deduced that both Information Gain with stacked MLR produced higher accuracy that the base learners’ results. Hence, this stacked model can be used for diagnosis of HNC in healthcare systems.