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Machine Learning-Based Analysis of Diagnostic Markers for Malnutrition
Nedim Ongun1, Mustafa Çakır2, Okan Oral3
1Department of Neurology, Antalya City Hospital, Antalya, Turkey, nedimongun@yahoo.com.
Annals of Nutrition & Metabolism
|December 2, 2025
Summary
A machine learning model accurately predicts malnutrition using patient data like hand grip strength and weight loss. This tool offers a faster, more efficient alternative for diagnosing malnutrition in clinical settings.
Area of Science:
- Clinical Nutrition
- Biostatistics
- Health Informatics
Background:
- Malnutrition is linked to adverse health outcomes, including prolonged recovery, extended hospital stays, and increased healthcare costs.
- Early and accurate diagnosis of malnutrition is crucial for improving patient outcomes and reducing healthcare expenditures.
Purpose of the Study:
- To develop and validate a predictive tool for diagnosing malnutrition.
- To leverage patient data, including anthropometric, phenotypic, and laboratory information, for malnutrition risk prediction.
Main Methods:
- A cohort of 252 adult patients was analyzed using logistic regression and decision tree analysis.
- Model performance was evaluated using metrics such as accuracy (89.8%) and kappa (0.79).
- Key predictors identified included hand grip strength, weight loss, BMI, and gender.
Main Results:
- Malnutrition was diagnosed in 69% of the 252 patients studied.
- The developed machine learning model demonstrated high predictive accuracy.
- Hand grip strength, weight loss, BMI, and gender were identified as the most significant indicators of malnutrition.
Conclusions:
- Machine learning models offer a superior and more efficient approach to malnutrition diagnosis compared to traditional methods.
- The validated model shows significant potential for enhancing the efficiency and accuracy of malnutrition diagnosis in clinical practice.
