Validation, improvement, and data mining of machine learning predictive models for large white male turkey body weight.
Uloženo v:
| Název: | Validation, improvement, and data mining of machine learning predictive models for large white male turkey body weight. |
|---|---|
| Autoři: | Flores, Karlinton R.1, Reading, Benjamin R.2, Grimes, Jesse L.1 jgrimes@ncsu.edu |
| Zdroj: | German Journal of Veterinary Research. 2025, Vol. 5 Issue 3, p42-58. 17p. |
| Druh dokumentu: | Article |
| Témata: | Machine learning, Data mining, Turkeys, Microbiota, Model validation, Body weight, Prediction models, Genetic variation |
| Author-Supplied Keywords: | Turkey performance Turkey production |
| Abstrakt: | Validation of predictive models is as critical as their development, and model improvement relies on the iterative incorporation of additional data. This process, however, can be costly, as experiments must adhere to consistent designs. Machine learning (ML) facilitates model validation by evaluating standard variables across data sets that were previously analyzed in isolation. Furthermore, ML enables the integration of new data into existing models and supports data mining (DM) to uncover novel patterns. The present study demonstrates the application of ML to validate, refine, and extend previously developed classification and regression models predicting body weight (BW) in Large White male turkeys. Performance, environmental, and microbiome data from two prior experiments, along with the original data set used for model construction, were analyzed. In total, 279 variables, including BW, gut pH, cloacal temperature, corn particle size, mineral source, genetic line, and microbiome taxa, were processed through ML and DM analyses. The Waikato Environment for Knowledge Analysis (WEKA) 3.8.5 Experimenter tool was used to test multiple classification and regression algorithms, employing 10-fold cross- validation, to predict BW at 18 weeks. Model validation across the three data sets yielded a correlation coefficient of 0.59 and a root mean square error (RMSE) of 1.03. Among the tested algorithms, M5' generated the most interpretable and practical model, achieving a correlation of 0.81 and an RMSE of 0.58 across four constructed models. Key predictors of 18-week BW included genetic line, rearing season, and BW at 5 and 14 weeks, while twelve microbiome taxa also contributed significantly to prediction accuracy. In conclusion, ML-based BW prediction models were successfully validated, improved, and expanded through DM approaches. Future research should focus on incorporating larger and more diverse data sets to enhance model robustness and predictive power. [ABSTRACT FROM AUTHOR] |
| Copyright of German Journal of Veterinary Research is the property of German Multidisciplinary Publishing Center and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Author Affiliations: | 1Prestage Department of Poultry Science, NC State University, Raleigh, North Carolina, USA 2Department of Applied Ecology, NC State University, Raleigh, North Carolina, USA |
| ISSN: | 2703-1322 |
| DOI: | 10.51585/gjvr.2025.3.0147 |
| Přístupové číslo: | 188822306 |
| Databáze: | Veterinary Source |
| Abstrakt: | Validation of predictive models is as critical as their development, and model improvement relies on the iterative incorporation of additional data. This process, however, can be costly, as experiments must adhere to consistent designs. Machine learning (ML) facilitates model validation by evaluating standard variables across data sets that were previously analyzed in isolation. Furthermore, ML enables the integration of new data into existing models and supports data mining (DM) to uncover novel patterns. The present study demonstrates the application of ML to validate, refine, and extend previously developed classification and regression models predicting body weight (BW) in Large White male turkeys. Performance, environmental, and microbiome data from two prior experiments, along with the original data set used for model construction, were analyzed. In total, 279 variables, including BW, gut pH, cloacal temperature, corn particle size, mineral source, genetic line, and microbiome taxa, were processed through ML and DM analyses. The Waikato Environment for Knowledge Analysis (WEKA) 3.8.5 Experimenter tool was used to test multiple classification and regression algorithms, employing 10-fold cross- validation, to predict BW at 18 weeks. Model validation across the three data sets yielded a correlation coefficient of 0.59 and a root mean square error (RMSE) of 1.03. Among the tested algorithms, M5' generated the most interpretable and practical model, achieving a correlation of 0.81 and an RMSE of 0.58 across four constructed models. Key predictors of 18-week BW included genetic line, rearing season, and BW at 5 and 14 weeks, while twelve microbiome taxa also contributed significantly to prediction accuracy. In conclusion, ML-based BW prediction models were successfully validated, improved, and expanded through DM approaches. Future research should focus on incorporating larger and more diverse data sets to enhance model robustness and predictive power. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 27031322 |
| DOI: | 10.51585/gjvr.2025.3.0147 |