Prediction of rhinitis with class imbalance based on heterogeneous ensemble learning

Comput Methods Biomech Biomed Engin. 2024 Apr 11:1-16. doi: 10.1080/10255842.2024.2339461. Online ahead of print.ABSTRACTCommon clinical rhinitis is characterized by different types of cases and class imbalance. Its prediction belongs to multiple output classification. Low recognition rate and poor generalization performance often occur for minority class. Therefore, we propose a novel integrated classification model, ARF-OOBEE, which transforms the multi-output classification to multi-label classification and multi-class classification. The multi-label classifier automatically adjusts the number and depth of integrated forest learners according to the imbalance ratio of single class label in a subset. It can effectively reduce the impact of class imbalance on classification and improve prediction performance of both majority or minority class concurrently. Also, we build a multi-class classification based on out-of-bag Extra-Tree to accomplish finer classification for the predicted labels. In addition, we calculate the feature importance for rhinitis on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features. We conduct 12 folds cross-validation experiments on 461 cases of clinical rhinitis. The outcomes show that the evaluation indicators of ARF-OOBEE, such as Sensitivity, Specificity, Accuracy, F1-Score, AUC, and G-Mean are 74.9%,86.5%,92.0%,78.3%,95.3%, and 79.9%, respectively. In comparison to th...
Source: Computer Methods in Biomechanics and Biomedical Engineering - Category: Biomedical Engineering Authors: Source Type: research