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Ramesh, L. (2025) Mia, N., T. Sarker, M. A. Halim, A. M. M. N. Alam, M. S. Ali, M. M. Rahman, and M. A. Hashem “Machine learning overview and its application in the livestock industry.” Meat Research 5, no. 1 ( 2025 ). CrossRef Google Scholar 2. Thangavel, Senthil Kumar, K. Somasundaram, and M. Ramasamy “Enhancing Milk Yield Forecasting in Dairy Farming Using an Interpretable Machine Learning Framework.” In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL), pp. 1549 - 1557. IEEE, 2025. View Article Google Scholar 3. Shi, Zhonghao, Fangyuan Chang, Yuanzheng Jia, Jun Li, Yawei Qiu, Jinfeng Miao, Wei Jiang, Xiaojun Guo, Xiangan Han, and Wei Tang “Classifying and understanding of dairy cattle health using wearable inertial sensors with random forest and explainable artificial intelligence.” IEEE Sensors Letters 8, no. 3 ( 2024 ): 1 - 4. View Article Google Scholar 4. Aqib, Amjad Islam, Mahreen Fatima, Afshan Muneer, Khazeena Atta, Muhammad Arslan, C-Neen Fatima Zaheer, Sadia Muneer, and Maheen Murtaza “Explainable Artificial Intelligence (XAI) in the Veterinary and Animal Sciences Field.” Explainable Artificial Intelligence for Biomedical Applications ( 2023 ): 33 - 56. CrossRef Google Scholar 5. Thangavel, Senthil Kumar, K. Somasundaram, and M. Ramasamy “Enhancing Milk Yield Forecasting in Dairy Farming Using an Interpretable Machine Learning Framework.” In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL), pp. 1549 - 1557. IEEE, 2025. View Article Google Scholar 6. Dilaver, Hatice, and Kamil Fatih Dilaver “The Transformative Impact of Artificial Intelligence and Sensor Technologies on Dairy Livestock Exports.” Black Sea Journal of Agriculture 8, no. 4 : 33 - 34. CrossRef Google Scholar 7. Mengisti Berihu Girmay, Felix Möhrle “Exploring explainability formats to aid decision-making in dairy farming systems.” In 44. GIL-Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, pp. 269 - 274. Gesellschaft für Informatik eV, 2024. Google Scholar 8. Ezanno, Pauline, Sébastien Picault, Gaël Beaunée, Xavier Bailly, Facundo Muñoz, Raphaël Duboz, Hervé Monod, and Jean-François Guégan “Research perspectives on animal health in the era of artificial intelligence.” Veterinary research 52, no. 1 ( 2021 ): 40. CrossRef Google Scholar 9. Khan, Malik Jahan, and Mian Muhammad Awais “Livestock Health Management Using Artificial Intelligence Based Approaches in Rural Areas.” In Smart Technologies for Sustainable Livestock Systems, pp. 173 - 192. CRC Press. CrossRef Google Scholar 10. Jin, Youn-Gyu, Guixin Wu, Ju-Won Seo, Seong-Jin Park, Sung-Ho Hur, Dinara Aliyeva, Jun-Hyung Park, and Kang-Min Kim “AI Veterinary Assistance: Enhancing Clinical Decision-making in Animal Healthcare.” IEEE Access ( 2025 ). View Article Google Scholar 11. Cockburn, Marianne “Application and prospective discussion of machine learning for the management of dairy farms.” Animals 10, no. 9 ( 2020 ): 1690. In: 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA).

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Official URL: https://ieeexplore.ieee.org/document/11170819

Abstract

Diabetes is a chronic metabolic disorder that
is rapidly increasing in the world, which makes early and
accurate prediction critical for timely intervention. The
study introduces a new predictive methodology called
Multi-Domain Fusion with Explainable Boosted Learning
(MF-EBL) for predicting diabetes using clinical,
demographic, and lifestyle datasets. The multi-domain
fusion inputs advanced methods of machine learning
(ML) including XGBoost, Random Forest(RF), Support

Vector Machine(SVM), Logistic Regression(LR), K-
Nearest Neighbors (KNN), and Neural Networks that

were evaluated according to accuracy, F1 score, AUC-
ROC, and explainability or interpretability of the ML

model. Overall, XGboost prediction model performed the
highest in all methods studied (accuracy of 88.7%, F1-
Score of 0.87 and AUC-ROC of 0.91). The SHAP is
applied to understand the interpretability of the model to
predict diabetes without losing model accuracy. The
study `conducted a data analysis and feature selection for
the all data included in the study to provide high
confidence for a robust model, and gained insight into the
key risk factors such as glucose levels, age and body mass
index (BMI). The MF-EBL framework presents a robust
and trustable solution for monitoring and early detection
of diabetes with results that are meaningful to clinicians
who are providing personalized care to patients and the
results can assist with decision-making not only through
monitoring and also with early risk assessment.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Computer Science Engineering > Big Data
Domains:	Computer Science Engineering
Depositing User:	Mr IR Admin
Last Modified:	13 May 2026 07:55
URI:	https://ir.vistas.ac.in/id/eprint/13785

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