A novel rhinitis prediction method for class imbalance. (August 2021)

• Record Type: Journal Article
• Authors: Yang, Jingdong; Zhang, Meng; Yu, Shaoqing
• Is Part Of: Biomedical signal processing and control. Volume 69(2021)
• Publication Date: 2021-08
• Subjects: Rhinitis prediction -- Class imbalance -- Cascaded under-sampling -- Ensemble learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102821
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We propose a cascaded under-sampling ensemble learning method to construct multiple batch classifiers, each of which is composed of a number of base classifiers with different structures. Through batch-by-batch under-sampling, the instances of class imbalance gradually reach the equalization. The average accuracy, true positive rate, and G-mean of the proposed model were 90.71 %, 87.44 %, and 88.18 %, respectively. Compared to typical classifiers, the proposed model has higher accuracy, true positive rate and lower missed diagnosis rate. We calculate the feature importance for rhinitis features on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features and classifications. Abstract: Rhinitis is a prevalent respiratory disease. Clinical rhinitis instances are characterized by multi-label and class imbalance, which is difficult to be accurately classified by typical machine learning methods. We propose a cascaded under-sampling ensemble learning method (CUEL) to construct multiple batch classifiers, each of which is composed of a few base classifiers with different structures. Through batch-by-batch under-sampling, the correctly classified instances of majority class are removed, and the samples that are difficult to classify are kept to gradually reach the equalization of class imbalance. We assign different weights to each of the batch classifiers to construct the final integrated classifier. Cross validation was performed on 2231 clinical rhinitis instances from Shanghai Tongji Hospital Affiliated to Tongji University . The experiment showed that the average accuracy, true positive rate, and G-mean of the CUEL model were 90.71 %, 87.44 %, and 88.18 %, respectively. Compared to typical classifiers, the CUEL model has higher accuracy, true positive rate and lower missed diagnosis rate, and has stronger generalization performance. It can make full use of all rhinitis instances and effectively reduce the prediction deviation caused by class imbalance. Therefore, it has a good auxiliary effect for the prevention and diagnosis of clinical rhinitis. In addition, we calculate the feature importance for rhinitis features on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features and classifications.