title: [Pre-warning model of bacterial foodborne illness based on performance of principal component analysis combined with support vector machine].
abstract: OBJECTIVE
  Based on the historical data of bacterial foodborne illness, the scoring system applied on the pre-warning model system was established in this study according to the rated harm factors. It could build up effectively the predictive model in the analysis of foodborne illness accidents.
  
  
  METHODS
  Extracting the useful information, the principal component analysis was performed on the normalized raw data to reduce the dimension. The result was split into 70% data randomly for training set into the regression model of support vector machine that was used to predict the remaining 30% .
  
  
  RESULTS
  Through reducing the dimensions for selecting the optional PCs, it could optimize the calibration and improve the efficiency. The combining method of principal component analysis (PCA) and support vector machine (SVM) could provide the reliable results in the pre-warning model, especially for the high-dimensional data with the limited sample populations. Furthermore, it could achieve 80% accuracy with the optimized parameters.
  
  
  CONCLUSIONS
  The pre-warning model of bacterial foodborne illness could give the assessment of the poisoning accidents and provided the scientific theory for reducing the incidence of bacterial of food poisoning.
authors.affiliation: Beijing Center of Disease Control and Prevention, Beijing 100013, China. dhjcdc@gmail.com
authors.forename: Hejun
authors.initials: H
authors.lastname: Duan
authors.forename: Bing
authors.initials: B
authors.lastname: Shao
mesh_terms: D002681:China; D003257:Consumer Product Safety; D005506:Food Contamination / Q000032:analysis*; D005516:Food Microbiology; D005517:Foodborne Diseases / Q000517:prevention & control*; D006801:Humans; D008461:Meat Products / Q000382:microbiology; D015233:Models, Statistical; D025341:Principal Component Analysis; D012307:Risk Factors; D060388:Support Vector Machine