Assessing the Incremental Value of Environment-Aware Bioelectrical Impedance Sensing for Machine Learning-Based Anemia Risk Monitoring in Goats

Bioelectrical impedance analysis (BIA) has been widely evaluated in livestock for body composition and physiological assessment, but its incremental value for anemia-risk monitoring in small ruminants remains unclear. This study evaluated BIA as part of an environmentally aware multimodal machine-learning framework for anemia-related monitoring in goats. Data from 81 Spanish male goats were restructured into animal-by-date observations and integrated with objective FAMACHA© classification, blood packed cell volume, gastrointestinal nematode fecal egg count, weather variables, temperature–humidity index, and raster-derived environmental covariates. Objective FAMACHA© was derived from packed cell volume and used as the primary classification endpoint. Binary, three-class, and four-class classification tasks were evaluated using animal-grouped cross-validation to reduce leakage from repeated observations. Binary low-risk versus high-risk classification was the most reliable task, whereas multiclass prediction showed limited resolution among adjacent FAMACHA© categories. A formal ablation analysis showed that BIA-only features achieved a ROC-AUC of 0.672, compared with 0.838 for fecal egg count plus weather/THI and 0.824 for the complete multimodal model. The full model did not significantly outperform the fecal egg count plus weather/THI model (ΔAUC = −0.013; 95% CI: −0.124 to 0.093; p = 0.844). A supplementary matched feature-block analysis likewise did not demonstrate a statistically reliable incremental improvement from adding BIA. Threshold analysis showed that a screening-oriented threshold detected 83.3% of high-risk observations with 71.3% specificity. These findings indicate that parasitological and environmental predictors provided the strongest discriminatory information, whereas the independent incremental predictive benefit of BIA remained unestablished. BIA should therefore be regarded as a potential supplementary physiological sensing modality rather than a validated primary predictor or replacement for established anemia-assessment methods.