Predicting the Cochlear Dead Regions Using a Machine Learning-Based Approach with Oversampling Techniques

Abstract

Background and Objectives: Determining the presence or absence of cochlear dead regions (DRs) is essential in clinical practice. This study proposes a machine learning (ML)-based model that applies oversampling techniques for predicting DRs in patients. Materials and Methods: We used recursive partitioning and regression for classification tree (CT) and logistic regression (LR) as prediction models. To overcome the imbalanced nature of the dataset, oversampling techniques to duplicate examples in the minority class or to synthesize new examples from existing examples in the minority class were adopted, namely the synthetic minority oversampling technique (SMOTE). Results: The accuracy results of the 10-fold cross-validation of the LR and CT with the original data were 0.82 (±0.02) and 0.93 (±0.01), respectively. The accuracy results of the 10-fold cross-validation of the LR and CT with the oversampled data were 0.66 (±0.02) and 0.86 (±0.01), respectively. Conclusions: This study is the first to adopt the SMOTE method to assess the role of oversampling methods on audiological datasets and to develop an ML-based model. Considering that the SMOTE method did not improve the model’s performance, a more flexible model or more clinical features may be needed.

Keywords

• cochlear dead region
• machine learning
• prediction model
• oversampling method
• synthetic minority oversampling technique