Prediction of rhinitis based on label-links classifier chain using feature-block

Rhinitis is a common chronic inflammation of the upper respiratory tract with multiple symptoms and signs. The clinical classification of rhinitis is a multi-label classification, characterized by high dimensionality, poor correlation and class imbalance. Low recognition rate and poor generalization performance often occur for minority class instances. Therefore, this paper proposes a Feature-Block classification model, MBLCC based on Label-Links Classifier Chain. We apply kernel density estimation and Deborah Hellinger distance to partition allergic rhinitis instance with similar characteristics, calculate the correlation matrix of label characteristics, build an ordered classification chain for each block, and integrate the predictions of each block classifiers by evidence theory as the outcome. The cross-validation experiments conducted on 2231 cases of clinical rhinitis show that the evaluation indicators of MBLCC, i.e. sensitivity, specificity, accuracy, F1-score, and G-Mean, are 91.80 %, 96.8 %, 96.9 %, 0.925, and 0.941 respectively. In comparison with the other baselines, MBLCC achieves better generalization performance and is more effective and rapid in early clinical diagnosis of rhinitis. In addition, we calculate the feature importance ranking for rhinitis features via Label-Links Classifier Chain on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features and classification that can provides the reference for the clinical rhinitis diagnosis.

Automated summary

This study proposes a Feature-Block classification model (MBLCC) based on Label-Links Classifier Chain for the diagnosis of allergic rhinitis. It partitions allergic rhinitis instances with similar characteristics, builds an ordered classification chain, and integrates the predictions of each block classifiers using evidence theory. The cross-validation experiments show that MBLCC achieves high evaluation indicators (sensitivity, specificity, accuracy, F1-score, and G-Mean) of 91.80%, 96.8%, 96.9%, 0.925, and 0.941 respectively. It outperforms other baselines in generalization performance and provides more effective and rapid diagnosis of rhinitis.