References
[1]Yorgancioğlu A, Kalayci O, Kalyoncu AF, Khaltaev N, Bousquet J. Allerjikrinitveastimüzerineetkisigüncelleme (ARIA 2008). TuberkToraks. 2008;56(2):224-231.
[2]Zhang Y, Zhang L. Prevalence of allergic rhinitis in china. Allergy Asthma Immunol Res. 2014;6(2):105-113.
[3]Zheng M, Wang X, Bo M, Wang K, Zhao Y, He F, Cao F, Zhang L, Bachert C. Prevalence of allergic rhinitis among adults in urban and rural areas of china: a population-based cross-sectional survey. Allergy Asthma Immunol Res. 2015;7(2):148-157.
[4]Skoner DP. Allergic rhinitis: definition, epidemiology, pathophysiology, detection, and diagnosis. J Allergy Clin Immunol. 2001;108(1 Suppl):S2-S8.
[5]Dordal MT, Lluch-Bernal M, Sánchez MC, Rondón C, Navarro A, Montoro J, Matheu V, Ibáñez MD, Fernández-Parra B, Dávila I, Conde J, Antón E, Colás C, Valero A. Allergen-specific nasal provocation testing: review by the rhinoconjunctivitis committee of the Spanish Society of Allergy and Clinical Immunology. J InvestigAllergol Clin Immunol. 2011;21(1):1-12.
[6]Chinoy B, Yee E, Bahna SL. Skin testing versus radioallergosorbent testing for indoor allergens. Clin Mol Allergy. 2005;3(1):4. Published 2005 Apr 15.
[7]Lloyd GA, Lund VJ, Scadding GK. CT of the paranasal sinuses and functional endoscopic surgery: a critical analysis of 100 symptomatic patients. J Laryngol Otol. 1991;105(3):181-185.
[8]Cheng L, Chen J, Fu Q, He S, Li H, Liu Z, Tan G, Tao Z, Wang D, Wen W, Xu R, Xu Y, Yang Q, Zhang C, Zhang G, Zhang R, Zhang Y, Zhou B, Zhu D, Chen L, Cui X, Deng Y, Guo Z, Huang Z, Huang Z, Li H, Li J, Li W, Li Y, Xi L, Lou H, Lu M, Ouyang Y, Shi W, Tao X, Tian H, Wang C, Wang M, Wang N, Wang X, Xie H, Yu S, Zhao R, Zheng M, Zhou H, Zhu L, Zhang L. Chinese Society of Allergy Guidelines for Diagnosis and Treatment of Allergic Rhinitis. Allergy Asthma Immunol Res. 2018;10(4):300-353.
[9]Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395(10236):1579-1586.
[10]Kaliner MA, Berger WE, Ratner PH, Siegel CJ. The efficacy of intranasal antihistamines in the treatment of allergic rhinitis. Ann Allergy Asthma Immunol. 2011;106(2 Suppl):S6-S11.
[11]Yan J, Zhang Z, Lin K, Yang F, Luo X. A hybrid scheme-based one-vs-all decision trees for multi-class classification tasks. Knowledge-Based Systems. 2020;198:105922.
[12]Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research. 2002;16(1):321-357.
[13]Zhao J, Jin J, Chen S, Zhang R, Yu B, Liu Q. A weighted hybrid ensemble method for classifying imbalanced data. Knowledge-Based Systems. 2020;203:106087.
[14]Zhang L, Shah SK, Kakadiaris IA. Hierarchical Multi-label Classification using Fully Associative Ensemble Learning. Pattern Recognition. 2017;70:89-103.
[15]Zhang H, Liu CT, Mao J, Shen C, Xie RL, Mu B. Development of novel in silico prediction model for drug-induced ototoxicity by using naïve Bayes classifier approach. Toxicol In Vitro. 2020;65:104812.
[16]Kumar S, Ong SH, Ranganath S, Ong TC, Chew FT. Classification of airspora using support vector machines (SVM). Journal of Allergy and Clinical Immunology. 2003;111(2):S91.
[17]Gao X, Hou J. An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process. Neurocomputing. 2016;174:906-911.
[18]Mondal P, Dey D, Chandra Saha N, Moitra S, Saha GK, Bhattacharya S, Podder S. Investigation of house dust mite induced allergy using logistic regression in West Bengal, India. World Allergy Organ J. 2019;12(12):100088. Published 2019 Nov 27.
[19]Heidari M, Shamsi H. Analog programmable neuron and case study on VLSI implementation of Multi-Layer Perceptron (MLP). Microelectronics Journal. 2019;84:36-47.
[20]Zhu G, Hu Q, Gu R, Yuan C, Huang Y. ForestLayer: Efficient training of deep forests on distributed task-parallel platforms. Journal of Parallel and Distributed Computing. 2019;132:113-126.
[21]Wang C, Deng C, Wang S. Imbalance-XGBoost: leveraging weighted and focal losses for binarylabel-imbalanced classification with XGBoost. Pattern Recognition Letters. 2020;136:190-197.
[22]Izquierdo-Verdiguier E, Zurita-Milla R. An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing. International Journal of Applied Earth Observation and Geoinformation. 2020;88:102051.
[23]Greiner AN, Hellings PW, Rotiroti G, Scadding GK. Allergic rhinitis. Lancet. 2011;378(9809):2112-2122.
[24]Wang X, Du K, She W, Ouyang Y, Sima Y, Liu C, Zhang L. Recent advances in the diagnosis of allergic rhinitis. Expert Rev Clin Immunol. 2018;14(11):957-964.
[25]Ullah R, Khan S, Ali H, Chaudhary II, Bilal M, Ahmad I. A comparative study of machine learning classifiers for risk prediction of asthma disease. PhotodiagnosisPhotodynTher. 2019;28:292-296.
[26]Segura-Bedmar I, Colón-Ruíz C, Tejedor-Alonso MÁ, Moro-Moro M. Predicting of anaphylaxis in big data EMR by exploring machine learning approaches. J Biomed Inform. 2018;87:50-59.
[27]Wide L, Bennich H, Johansson SG. Diagnosis of allergy by an in-vitro test for allergen antibodies. Lancet. 1967;2(7526):1105-1107.
[28]Waring J, Lindvall C, Umeton R. Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. ArtifIntell Med. 2020;104:101822.
[29]Kalaiselvi B, Thangamani M. An efficient Pearson correlation based improved random forest classification for protein structure prediction techniques. Measurement. 2020;162:107885.
[30]Kadkhodaei HR, Moghadam AME, Dehghan M. HBoost: A heterogeneous ensemble classifier based on the Boosting method and entropy measurement. Expert Systems with Applications. 2020;157:113482.
[31]Bhardwaj R, Hooda N. Prediction of Pathological Complete Response after Neoadjuvant Chemotherapy for breast cancer using ensemble machine learning. Informatics in Medicine Unlocked. 2019;16:100219.
Table 1 classification labels of diseases