Abstract
Diagnostic modeling of voice disorders is important to improve the
efficiency of patient diagnosis. However, due to sample imbalance and
complexity of voice data analysis, basic acoustic features and
traditional machine learning methods are not effective in predicting
voice status. This letter proposes a three-layer ensemble learning model
with two innovations: 1) At the feature level, acoustic features are
expanded using Multilayer Perceptron and SincNet; 2) Structurally, a
tree-based ensemble learning model is proposed that utilizes XGBoost for
feature selection and feature transformation, with LightGBM as the
classifier. Experiments have shown that the proposed method in this
paper outperforms traditional machine learning models.