AUTHOR=Marzougui Afef , McConnel Craig S. , Adams-Progar Amber , Biggs Tyler D. , Ficklin Stephen P. , Sankaran Sindhuja 

TITLE=Machine learning-derived cumulative health measure for assessing disease impact in dairy cattle

JOURNAL=Frontiers in Animal Science

VOLUME=Volume 6 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/animal-science/articles/10.3389/fanim.2025.1532385

DOI=10.3389/fanim.2025.1532385

ISSN=2673-6225

ABSTRACT=Dairy cattle’s susceptibility to diseases significantly impacts their health, welfare, and longevity. Disability weights reflect the relative severity or impact of important diseases and provide an extension of epidemiological frequency measures. They are central for comparing disease burden across diverse causes when summarizing health status and disease severity. Yet, they often reflect group-level health status and rely on expert judgment, which is subjective. In absence of an objective approach, this study aimed to create disability weight metrics using pathophysiological data with machine learning approach. Four binary classifiers using a generalized linear model with Lasso regularization were developed to identify distinguishing features for healthy and diseased cows affected by hypocalcemia, ketosis, metritis and mastitis. Model performance, assessed via the Area Under the Curve (AUC), reached values of 0.72, 0.66, 0.82, and 0.92 for distinguishing hypocalcemia, ketosis, metritis and mastitis in cows from healthy groups. The selected features were combined into a summary disability weight – cumulative health measure – for each disease computed through weighted sums of feature importance from classification models. Notably, the average cumulative health measure differed significantly between healthy and diseased groups (p < 0.05). The relative ranking of diseases based on the average cumulative health measure was comparable to the expert survey-based approach. Such features will offer insights into disease impact and will provide a standardized metric for comparing disease severity.