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Artificial Intelligence Applications in allergic rhinitis diagnosis: Focus on Ensemble Learning
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  • Fu Dai,
  • Yifei Meng ,
  • Shiwang Tan ,
  • peng Liu ,
  • Chuanliang Zhao ,
  • yue Qian,
  • Jingdong Yang,
  • Shaoqing YU
Fu Dai
Department of Otorhinolaryngology, Antin Hospital, Shanghai 200065, China
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Yifei Meng
University of Shanghai for Science and Technology
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Shiwang Tan
Tongji University
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peng Liu
Department of Otorhinolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji University School of Medicine
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Chuanliang Zhao
Department of Otorhinolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji University School of Medicine, Shanghai 200065, China
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yue Qian
Department of Otorhinolaryngology, Antin Hospital, Shanghai 200065, China
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Jingdong Yang
University of Shanghai for Science and Technology
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Shaoqing YU
Tongji Hospital, Tongji University, Shanghai, China
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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´╝ÜRhinosinusitis, 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.