Risk factor analysis of foodborne pathogen infection using statistic and soft computing approaches
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".