Artificial intelligence applications in allergic rhinitis diagnosis: Focus on ensemble learning.

Abstract

Background: The diagnosis of allergic rhinitis (AR) primarily relies on symptoms and laboratory examinations. Due to limitations in outpatient settings, certain tests such as nasal provocation tests and nasal secretion smear examinations are not routinely conducted. Although there are clear diagnostic criteria, an accurate diagnosis still requires the expertise of an experienced doctor, considering the patient's medical history and conducting examinations. However, differences in physician knowledge and limitations of examination methods can result in variations in diagnosis.

Objective: Artificial intelligence is a significant outcome of the rapid advancement in computer technology today. This study aims to present an intelligent diagnosis and detection method based on ensemble learning for AR.

Method: We conducted a study on AR cases and 7 other diseases exhibiting similar symptoms, including rhinosinusitis, chronic rhinitis, upper respiratory tract infection, etc. Clinical data, encompassing medical history, clinical symptoms, allergen detection, and imaging, was collected. To develop an effective classifier, multiple models were employed to train on the same batch of data. By utilizing ensemble learning algorithms, we obtained the final ensemble classifier known as adaptive random forest-out of bag-easy ensemble (ARF-OOBEE). In order to perform comparative experiments, we selected 5 commonly used machine learning classification algorithms: Naive Bayes, support vector machine, logistic regression, multilayer perceptron, deep forest (GC Forest), and extreme gradient boosting (XGBoost).To evaluate the prediction performance of AR samples, various parameters such as precision, sensitivity, specificity, G-mean, F1-score, and area under the curve (AUC) of the receiver operating characteristic curve were jointly employed as evaluation indicators.

Results: We compared 7 classification models, including probability models, tree models, linear models, ensemble models, and neural network models. The ensemble classification algorithms, namely ARF-OOBEE and GC Forest, outperformed the other algorithms in terms of the comprehensive classification evaluation index. The accuracy of G-mean and AUC parameters improved by nearly 2% when compared to the other algorithms. Moreover, these ensemble classifiers exhibited excellent performance in handling large-scale data and unbalanced samples.

Conclusion: The ARF-OOBEE ensemble learning model demonstrates strong generalization performance and comprehensive classification abilities, making it suitable for effective application in auxiliary AR diagnosis.