Application of Stacked Ensemble Techniques on Ensemble Feature Selection Techniques for Classifying Recurrent Head and Neck Squamous Cell Carcinoma Prognosis

Abstract

This study aimed to identify the optimal combination of the stacked ensemble (SE) and the heterogeneous ensemble feature selection (HETR-EFS) technique for classifying HNSCC recurrence patterns. Four SE classification models were developed based on various EFS techniques, using GBM meta-classifiers in each case. The results showed that implementing the SE technique consisting of five base classifiers on the heterogeneous ensemble feature (HETR-EF) subset achieved better performance than other EF subsets and HETR-EFs. Thus, learning SE technique having five base classifiers on HETR-EFs is clinically appropriate as a prognostic model for classifying and predicting HNSCC patients' recurrence data. The SE technique, which combines base classifier models, is clinically appropriate for classifying and predicting HNSCC patients' recurrence data. The study highlights the importance of finding a machine learning algorithm that performs best given varied distributions, as not all algorithms are equally created.