Objective:The annual influenza epidemic is a heavy burden on the health care system,and has increasingly become a major public health problem in some areas,such as Hong Kong(China).Therefore,based on a variety of machine learning methods,and considering the seasonal influenza in Hong Kong,the study aims to establish a Combinatorial Judgment Classifier(CJC)model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.Methods:The characteristic variables were selected using the single-factor statistical method to establish the influencing factor system of an influenza outbreak.On this basis,the CJC model was proposed to provide an early warning for an influenza outbreak.The characteristic variables in the final model included atmospheric pressure,absolute maximum temperature,mean temperature,absolute minimum temperature,mean dew point temperature,the number of positive detections of seasonal influenza viruses,the positive percentage among all respiratory specimens,and the admission rates in public hospitals with a principal diagnosis of influenza.Results:The accuracy of the CJC model for the influenza outbreak trend reached 96.47％,the sensitivity and specificity change rates of this model were lower than those of other models.Hence,the CJC model has a more stable prediction performance.In the present study,the epidemic situation and meteorological data of Hong Kong in recent years were used as the research objects for the construction of the model index system,and a lag correlation was found between the influencing factors and influenza outbreak.However,some potential risk factors,such as geographical nature and human factors,were not incorporated,which ideally affected the prediction performance to some extent.Conclusion:In general,the CJC model exhibits a statistically better performance,when compared to some classical early warning algorithms,such as Support Vector Machine,Discriminant Analysis,and Ensemble Classfiers,which improves the performance of the early warning of seasonal influenza.

Zi-xiao WANG;James NTAMBARA;Yan LU;Wei DAI;Rui-jun MENG;Dan-min QIAN

Department of Medical Informatics,School of Medicine,Nantong University,Nantong 226001,China;Department of Computer Science,College of Engineering and Computing Sciences,New York Institute of Technology,New York 10023,USA;Department of Computer Science,College of Overseas Education,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;Department of Epidemiology,School of Public Health,Nantong University,Nantong 226019,China;Artificial Intelligence Laboratory Center,De Montfort University of Leicester,Leicester LE1 9BH,United Kingdom

当代医学科学(英文)

[1]Zi-xiao WANG;James NTAMBARA;Yan LU;Wei DAI;Rui-jun MENG;Dan-min QIAN-.Construction of Influenza Early Warning Model Based on Combinatorial Judgment Classifier:A Case Study of Seasonal Influenza in Hong Kong)[J].当代医学科学(英文),2022(01):226-236

Judgment,CJC,Classfiers

Construction,Influenza,Early,Warning,Model,Based,Combinatorial,Classifier,Case,Study,Seasonal,Hong,Kong,Objective,annual,influenza,epidemic,heavy,burden,health,care,system,has,increasingly,become,major,public,problem,some,areas,such,China,Therefore,variety,machine,learning,methods,considering,seasonal,study,aims,establish,classify,trend,accuracy,early,warning,Methods,characteristic,variables,were,selected,using,single,influencing,outbreak,On,this,basis,was,proposed,provide,final,included,atmospheric,pressure,absolute,maximum,temperature,mean,minimum,dew,point,number,positive,detections,viruses,percentage,among,respiratory,specimens,admission,rates,hospitals,principal,diagnosis,Results,reached,sensitivity,specificity,change,lower,than,those,other,models,Hence,more,stable,prediction,performance,present,situation,meteorological,data,recent,years,used,research,objects,construction,lag,correlation,found,between,factors,However,potential,risk,geographical,nature,human,not,incorporated,which,ideally,affected,extent,Conclusion,general,exhibits,statistically,better,when,compared,classical,algorithms,Support,Vector,Machine,Discriminant,Analysis,Ensemble,improves

0.50677