Vet World   Vol.18   November-2025  Article - 8 

Machine learning-driven analysis of feed additives and intestinal microbiota diversity in broiler chickens: Clustering of mineral profiles and predictive diet modeling

Background and Aim: The gut microbiota of broilers plays a pivotal role in nutrient absorption, immune modulation, and mineral metabolism. Feed additives can influence these microbial and physiological processes, yet their integrated effects remain insufficiently understood. This study aimed to intelligently evaluate the impact of various feed additives on the intestinal microbiota and mineral composition of broiler chickens and to develop machine learning (ML) models for clustering and classification of diet-associated mineral and microbial profiles.

Materials and Methods: A total of 385 Arbor Acres broilers (7 days old) were allocated into 11 groups, including one control semi-synthetic diet (SSD), one group with a semi-synthetic deficient diet (SSDD), and nine experimental groups receiving SSDD with different additives: Probiotics (Soya-bifidum and Sporobacterin), dietary fibers (cellulose, lactulose, and chitosan), enterosorbents (enterosgel and activated carbon), and ultrafine particles (UFPs) (Cu and Fe). Microbiota composition was assessed by 16S ribosomal RNA sequencing, and body mineral composition was determined through inductively coupled plasma mass spectrometer. To overcome data scarcity, synthetic records were generated using conditional tabular generative adversarial networks. K-means and hierarchical agglomerative clustering were used for mineral profile grouping, while logistic regression, SVM, and decision tree models classified diet types.

Results: Hierarchical clustering revealed six distinct mineral profile groups (Silhouette = 0.524), with SSD and SSDD forming separate clusters. Feed additives such as UFPs, chitosan, and activated carbon induced similar mineral patterns. Key differentiating biomarkers were cobalt, zinc, strontium, arsenic, and lithium (p < 0.05). The decision tree classifier achieved 74% accuracy in predicting diet types based on microbiota data. Alpha diversity analysis showed enhanced microbial richness in groups fed lactulose, enterosgel, cellulose, or activated carbon.

Conclusion: ML effectively elucidated complex relationships between diet, microbiota composition, and mineral metabolism in broilers. The integration of clustering and predictive models demonstrates the feasibility of intelligent feeding systems tailored to optimize gut health and nutrient utilization. Future studies integrating multi-omics data and broader farm-level validation will strengthen precision nutrition frameworks for sustainable poultry production.

Keywords: broilers, clustering, conditional tabular generative adversarial networks, decision tree, feed additives, gut microbiota, machine learning, mineral metabolism.

How to cite this article: Grishina LS, Zhigalov AY, Bolodurina IP, Shukhman AE, Niryan PL, Kvan OV, Sheida EV (2025) Machine learning-driven analysis of feed additives and intestinal microbiota diversity in broiler chickens: Clustering of mineral profiles and predictive diet modeling, Veterinary World, 18(11): 3390-3408.

Received: 21-07-2025   Accepted: 29-09-2025   Published online: 06-11-2025

Corresponding author: Lyubov Sergeevna Grishina    E-mail: grishina_ls@inbox.ru

DOI: 10.14202/vetworld.2025.3390-3408

Copyright: Grishina, et al. This article is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http:// creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.