ArticleFrontiers in genetics2026
Investigation of factors affecting egg breakage resistance in laying hens using data mining and machine learning methods.
Article in Frontiers in genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: This study aims to evaluate the predictive performance of different machine learning algorithms and to identify the key factors influencing eggshell breaking resistance in laying hens. Methods: Three machine learning methods, C5.0 decision tree, Random Forest (RF), and Support Vector Regression (SVR), were applied to egg classification and prediction tasks. Eggshell strength (ER) was predicted using egg weight (EW), shell weight (SW), shell thickness (ST), and shape index (SI). Results: The C5.0 decision tree, trained on 464 eggs, achieved an overall classification accuracy of 65.1%, with a tendency to misclassify brown eggs as white, suggesting potential feature overlap or class imbalance. Among the regression models, RF outperformed SVR, yielding higher R Discussion: Overall, the findings indicate that ensemble-based machine learning methods are effective tools for both accurate prediction and identification of influential factors related to eggshell strength.
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