Artificial Intelligence Applications in allergic rhinitis diagnosis:
Focus on Ensemble Learning
Abstract
Background and purpose: Artificial intelligence is an important product
of the rapid development of computer technology today. This study
intends to propose an intelligent diagnosis and detection method for AR
based on ensemble learning. Method: This study collectedAR cases and
other 7 types of diseases with similar symptoms:Rhinosinusitis, Chronic
rhinitis, upper respiratory tract infection etc.) and collected clinical
data such as medical history, clinical symptoms, allergen detection and
imaging. Multiple models are used to train the classifier for the same
batch of data, and the final ensemble classifier is obtained by using
the ensemble learning algorithm. 5 common machine learning
classification algorithms were selected for comparative experiments,
including Naive Bayes (NB), Support Vector Machine (SVM), Logistic
Regression (LR), Multilayer Perceptron (MLP), Deep Forest (GCForest),
eXtreme Gradient boosting (XGBoost). In order to evaluate the prediction
results of AR samples, parameters such as Precision, Sensitivity,
Specificity, G-Mean, F1-Score, and AUC under the ROC curve are jointly
used as prediction evaluation indicators. Results: 7 classification
models are used for comparison, covering probability model, tree model,
linear model, ensemble model and neural network models, and the
comprehensive classification evaluation index is lower than the ensemble
classification algorithms ARF-OOBEE and GCForest. Compared with other
algorithms, the accuracy of G-Mean and AUC parameters is improved nearly
2%, and it has good comprehensive classification characteristics for
massive large data and unbalanced samples. Conclusion: The ensemble
learning ARF-OOBEE model has good generalization performance and
comprehensive classification ability to be used for diagnosis of AR.