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MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

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Early Identification of Individual Nursery Pigs at Risk of Requiring Health Treatment Using Machine Learning.

Saiara Samira Sajid1, Guiping Hu2, John C S Harding3

  • 1Department of Industrial, Manufacturing & Systems Engineering, Iowa State University, Ames, IA, United States.

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This study developed a prediction model using daily pig feeding, drinking, and weight data to identify pigs at higher risk for health treatments in the swine industry. While models showed limited ability to predict treatment numbers, they could rank pigs by risk, with drinking data being most informative.

Keywords:
body weightdisease predictiondrinkingfeedingmachine learningpigs

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Area of Science:

  • Animal Science
  • Veterinary Medicine
  • Machine Learning in Agriculture

Background:

  • Infectious diseases pose significant challenges to the swine industry, impacting production efficiency and animal welfare.
  • Early identification of pigs at risk of requiring health treatments is crucial for timely intervention and disease management.
  • Dynamic disease environments in commercial settings necessitate robust prediction models for proactive health strategies.

Purpose of the Study:

  • To develop and evaluate a prediction model for early identification of late nursery pigs at higher risk of requiring health treatments.
  • To utilize daily feeding, drinking, and body weight data for predicting treatment needs in a natural disease challenge environment.
  • To assess the performance of various machine learning models in ranking pigs based on their predicted probability of requiring treatment.

Main Methods:

  • A unique dataset comprising 21 batches of up to 75 late nursery pigs was used.
  • Daily feeding, drinking, and body weight data were collected from days 6 to 14 post-exposure.
  • Four tree-based machine learning models and an ensemble model were employed using a leave-one-batch-out validation approach.

Main Results:

  • Models demonstrated a limited ability to predict the exact number of pigs requiring treatment due to dynamic disease challenges.
  • All models showed capability in ranking pigs by their probability of requiring treatment, with generally positive correlations to outcomes.
  • Random Forest model exhibited the highest prediction performance; drinking data offered slightly more predictive information than feeding data.

Conclusions:

  • Early daily data on feeding, drinking, and body weight can partially identify nursery pigs at higher risk for health treatments.
  • Additional data features or human observations are likely necessary to significantly improve the accuracy of early risk identification.
  • The developed models provide a basis for ranking pigs by health risk, aiding in targeted management strategies within swine production.