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Risk factor analysis of foodborne pathogen infection using statistic and soft computing approaches.

机译：使用统计和软计算方法对食源性病原体感染的危险因素进行分析。

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摘要

To develop appropriate prevention and control strategies for sporadic cases of illness, it is important to accurately model the system and analyze the risk factors. The objective of this study is to utilize both statistic and soft computing models to identify the significant risk factors for Salmonella Typhimurium DT104 and non-DT104 infection in Canada, and compare the findings. Previous studies have focused on analyzing each risk factor separately using single variable analysis, or modelling multiple risk factors using statistic models, such as logistic regression models. In this study, both neural network models and statistic models are developed and compared to determine which method produces superior results.;Genetic algorithms are further incorporated to extract the optimal subset of factors that provide an accurate classification. The genetic algorithm based neural classifier significantly outperform the statistic models and neural networks alone because either statistic models or neural networks alone are not able to consider factors' nonlinear interaction with maximum likelihood estimate, which selects the significant risk factor based on likelihood ratio test. A neuro-fuzzy based method for predicting Salmonella Typhimurium infections is further proposed.;In addition, neural network models are developed to study the effect of climatic factors for Salmonella infections. Simulation studies show that neural networks perform better than corresponding linear, quadratic and cubic regression models in terms of correlation coefficients between Salmonella infections and climate factors.
机译：要针对散发的疾病制定适当的预防和控制策略，准确建立系统模型并分析风险因素非常重要。这项研究的目的是利用统计和软计算模型来确定加拿大鼠伤寒沙门氏菌DT104和非DT104感染的重要危险因素，并比较研究结果。先前的研究集中在使用单变量分析分别分析每个风险因素，或使用统计模型（例如逻辑回归模型）对多个风险因素进行建模。在这项研究中，开发了神经网络模型和统计模型，并进行了比较，以确定哪种方法可产生更好的结果。进一步整合了遗传算法，以提取因素的最优子集，以提供准确的分类。基于遗传算法的神经分类器明显优于单独的统计模型和神经网络，因为单独的统计模型或神经网络都无法考虑因素与最大似然估计的非线性相互作用，从而基于似然比检验选择重要的危险因素。进一步提出了一种基于神经模糊的鼠伤寒沙门氏菌感染预测方法。此外，建立了神经网络模型来研究气候因素对沙门氏菌感染的影响。仿真研究表明，在沙门氏菌感染与气候因素之间的相关系数方面，神经网络的性能优于相应的线性，二次和三次回归模型。

著录项

  • 作者

    Qin, Lixu.;

  • 作者单位

    University of Guelph (Canada).;

  • 授予单位 University of Guelph (Canada).;
  • 学科 Engineering General.;Artificial Intelligence.
  • 学位 M.A.Sc.
  • 年度 2009
  • 页码 95 p.
  • 总页数 95
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 工程基础科学;人工智能理论;
  • 关键词

  • 入库时间 2022-08-17 11:38:24

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