Machine Learning-Based Analysis of Diagnostic Markers for Malnutrition
Machine learning model offers a faster, more efficient alternative to traditional diagnostic tools. The study concludes that machine learning-based models provide superior prediction performance and could significantly enhance the efficiency and accuracy of malnutrition diagnosis in healthcare.
Authors
- Mesut Yılmaz (ORCID: https://orcid.org/0000-0001-8799-3452)
- Nedim Ongun (ORCID: https://orcid.org/0000-0003-1694-5933)
- Okan Oral (ORCID: https://orcid.org/0000-0002-6302-4574)
- Mustafa Çakır (ORCID: https://orcid.org/0000-0002-1794-9242)
- Sinem Güzel
Institutions
- Akdeniz University (TR)
- Antalya Eğitim ve Araştırma Hastanesi (TR)
- İskenderun Technical University (TR)
- Antalya IVF (TR)
Publication Details
- Journal
- Annals of Nutrition and Metabolism
- Published
- 2025-12-02
- DOI
- https://doi.org/10.1159/000549549
- Primary Topic
- Child Nutrition and Water Access
- Type
- article
- Field-Weighted Citation Impact
- 0.00