This study developed a mathematical model using hair biomarkers (levels of Mg, K, Fe, Al, Cr) to noninvasively predict iron content in Hereford cattle muscle tissue, potentially improving livestock management and meat quality.
This study utilized the Random Forest Algorithm to create a machine learning model aimed at accurately predicting hair loss by considering complex datasets involving genetic, hormonal, lifestyle, and environmental factors, but specific outcomes were not reported.
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March 2022 in “Frontiers in Endocrinology” This study developed a mathematical model and online tool using serum AMH, androstenedione levels, UML, and BMI to screen for undiagnosed PCOS, particularly useful for Asian populations.
This study evaluated machine-learning models to predict PCOS among reproductive-aged women in Bangladesh, finding that the XGBoost model achieved high accuracy (99.63%) and effectiveness, particularly when prioritizing clinical features over psychological ones in the predictive process.
November 2025 in “Frontiers in Animal Science” This study established a predictive model for water intake in hair sheep, finding significant associations with dry matter and its intake but not with sex classes, suggesting a more accurate and efficient way to predict and manage water use in these animals.