Machine Learning Approach to Assessment of Nutritional Status of Children with Cardiac Disease in Ethiopia

Tayu Nigusie Abebe; Abdisa Jura Hunduma

doi:doi:10.11648/j.ijdst.20261203.12

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Machine Learning Approach to Assessment of Nutritional Status of Children with Cardiac Disease in Ethiopia

Tayu Nigusie Abebe^*

, Abdisa Jura Hunduma

Published in International Journal on Data Science and Technology (Volume 12, Issue 3)

Received: 1 August 2026 Accepted: 4 September 2026 Published: 22 September 2026

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Abstract

Nutritional status is an important determinant of health outcomes among children with cardiac disease, yet identifying children at risk of poor nutritional status remains challenging. Machine learning methods may provide useful tools for identifying important predictors and improving the classification of nutritional status in pediatric cardiac patients. The main objective of the study is to predict the nutritional status of children with cardiac disease using various machine learning methods, such as decision tree, logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting. The predictive performance of each machine learning method was evaluated based on precision, accuracy, recall, f1 score, and the Kappa statistics value. The results indicate that the eXtreme Gradient Boosting method is recommended for predicting children nutritional status with precision, accuracy, recall, f1 score, and kappa statistics values: 0.7812, 0.7995, 0.7799, 0.7884, and 0.6299, respectively. The features that determine the nutritional status of children with cardiac disease are ROSS/NYHA, types of cardiac disease, pulmonary hypertension, anemia, pneumonia, and residence. This research will assist policy makers and healthcare providers to develop a framework for implementing necessary interventions and care practices to prevent complications associated with nutritional status in cardiac patients.

Published in	International Journal on Data Science and Technology (Volume 12, Issue 3)
DOI	10.11648/j.ijdst.20261203.12
Page(s)	64-74
Creative Commons	This 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 or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Machine Learning, Nutritional Status, Cardiac, Children, Prediction

References

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APA Style

Abebe, T. N., Hunduma, A. J. (2026). Machine Learning Approach to Assessment of Nutritional Status of Children with Cardiac Disease in Ethiopia. International Journal on Data Science and Technology, 12(3), 64-74. https://doi.org/10.11648/j.ijdst.20261203.12

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ACS Style

Abebe, T. N.; Hunduma, A. J. Machine Learning Approach to Assessment of Nutritional Status of Children with Cardiac Disease in Ethiopia. Int. J. Data Sci. Technol. 2026, 12(3), 64-74. doi: 10.11648/j.ijdst.20261203.12

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AMA Style

Abebe TN, Hunduma AJ. Machine Learning Approach to Assessment of Nutritional Status of Children with Cardiac Disease in Ethiopia. Int J Data Sci Technol. 2026;12(3):64-74. doi: 10.11648/j.ijdst.20261203.12

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@article{10.11648/j.ijdst.20261203.12,
  author = {Tayu Nigusie Abebe and Abdisa Jura Hunduma},
  title = {Machine Learning Approach to Assessment of Nutritional Status of Children with Cardiac Disease in Ethiopia},
  journal = {International Journal on Data Science and Technology},
  volume = {12},
  number = {3},
  pages = {64-74},
  doi = {10.11648/j.ijdst.20261203.12},
  url = {https://doi.org/10.11648/j.ijdst.20261203.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20261203.12},
  abstract = {Nutritional status is an important determinant of health outcomes among children with cardiac disease, yet identifying children at risk of poor nutritional status remains challenging. Machine learning methods may provide useful tools for identifying important predictors and improving the classification of nutritional status in pediatric cardiac patients. The main objective of the study is to predict the nutritional status of children with cardiac disease using various machine learning methods, such as decision tree, logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting. The predictive performance of each machine learning method was evaluated based on precision, accuracy, recall, f1 score, and the Kappa statistics value. The results indicate that the eXtreme Gradient Boosting method is recommended for predicting children nutritional status with precision, accuracy, recall, f1 score, and kappa statistics values: 0.7812, 0.7995, 0.7799, 0.7884, and 0.6299, respectively. The features that determine the nutritional status of children with cardiac disease are ROSS/NYHA, types of cardiac disease, pulmonary hypertension, anemia, pneumonia, and residence. This research will assist policy makers and healthcare providers to develop a framework for implementing necessary interventions and care practices to prevent complications associated with nutritional status in cardiac patients.},
 year = {2026}
}

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TY  - JOUR
T1  - Machine Learning Approach to Assessment of Nutritional Status of Children with Cardiac Disease in Ethiopia
AU  - Tayu Nigusie Abebe
AU  - Abdisa Jura Hunduma
Y1  - 2026/09/22
PY  - 2026
N1  - https://doi.org/10.11648/j.ijdst.20261203.12
DO  - 10.11648/j.ijdst.20261203.12
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 64
EP  - 74
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20261203.12
AB  - Nutritional status is an important determinant of health outcomes among children with cardiac disease, yet identifying children at risk of poor nutritional status remains challenging. Machine learning methods may provide useful tools for identifying important predictors and improving the classification of nutritional status in pediatric cardiac patients. The main objective of the study is to predict the nutritional status of children with cardiac disease using various machine learning methods, such as decision tree, logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting. The predictive performance of each machine learning method was evaluated based on precision, accuracy, recall, f1 score, and the Kappa statistics value. The results indicate that the eXtreme Gradient Boosting method is recommended for predicting children nutritional status with precision, accuracy, recall, f1 score, and kappa statistics values: 0.7812, 0.7995, 0.7799, 0.7884, and 0.6299, respectively. The features that determine the nutritional status of children with cardiac disease are ROSS/NYHA, types of cardiac disease, pulmonary hypertension, anemia, pneumonia, and residence. This research will assist policy makers and healthcare providers to develop a framework for implementing necessary interventions and care practices to prevent complications associated with nutritional status in cardiac patients.
VL  - 12
IS  - 3
ER  -

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Author Information

Tayu Nigusie Abebe

Department of Statistics, Bule Hora University, Bule Hora, Ethiopia

Contact Email

http://orcid.org/0009-0009-3602-6484
Abdisa Jura Hunduma

Department of Statistics, Bule Hora University, Bule Hora, Ethiopia

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APA Style

Abebe, T. N., Hunduma, A. J. (2026). Machine Learning Approach to Assessment of Nutritional Status of Children with Cardiac Disease in Ethiopia. International Journal on Data Science and Technology, 12(3), 64-74. https://doi.org/10.11648/j.ijdst.20261203.12

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ACS Style

Abebe, T. N.; Hunduma, A. J. Machine Learning Approach to Assessment of Nutritional Status of Children with Cardiac Disease in Ethiopia. Int. J. Data Sci. Technol. 2026, 12(3), 64-74. doi: 10.11648/j.ijdst.20261203.12

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AMA Style

Abebe TN, Hunduma AJ. Machine Learning Approach to Assessment of Nutritional Status of Children with Cardiac Disease in Ethiopia. Int J Data Sci Technol. 2026;12(3):64-74. doi: 10.11648/j.ijdst.20261203.12

Copy | Download

@article{10.11648/j.ijdst.20261203.12,
  author = {Tayu Nigusie Abebe and Abdisa Jura Hunduma},
  title = {Machine Learning Approach to Assessment of Nutritional Status of Children with Cardiac Disease in Ethiopia},
  journal = {International Journal on Data Science and Technology},
  volume = {12},
  number = {3},
  pages = {64-74},
  doi = {10.11648/j.ijdst.20261203.12},
  url = {https://doi.org/10.11648/j.ijdst.20261203.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20261203.12},
  abstract = {Nutritional status is an important determinant of health outcomes among children with cardiac disease, yet identifying children at risk of poor nutritional status remains challenging. Machine learning methods may provide useful tools for identifying important predictors and improving the classification of nutritional status in pediatric cardiac patients. The main objective of the study is to predict the nutritional status of children with cardiac disease using various machine learning methods, such as decision tree, logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting. The predictive performance of each machine learning method was evaluated based on precision, accuracy, recall, f1 score, and the Kappa statistics value. The results indicate that the eXtreme Gradient Boosting method is recommended for predicting children nutritional status with precision, accuracy, recall, f1 score, and kappa statistics values: 0.7812, 0.7995, 0.7799, 0.7884, and 0.6299, respectively. The features that determine the nutritional status of children with cardiac disease are ROSS/NYHA, types of cardiac disease, pulmonary hypertension, anemia, pneumonia, and residence. This research will assist policy makers and healthcare providers to develop a framework for implementing necessary interventions and care practices to prevent complications associated with nutritional status in cardiac patients.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Machine Learning Approach to Assessment of Nutritional Status of Children with Cardiac Disease in Ethiopia
AU  - Tayu Nigusie Abebe
AU  - Abdisa Jura Hunduma
Y1  - 2026/09/22
PY  - 2026
N1  - https://doi.org/10.11648/j.ijdst.20261203.12
DO  - 10.11648/j.ijdst.20261203.12
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 64
EP  - 74
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20261203.12
AB  - Nutritional status is an important determinant of health outcomes among children with cardiac disease, yet identifying children at risk of poor nutritional status remains challenging. Machine learning methods may provide useful tools for identifying important predictors and improving the classification of nutritional status in pediatric cardiac patients. The main objective of the study is to predict the nutritional status of children with cardiac disease using various machine learning methods, such as decision tree, logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting. The predictive performance of each machine learning method was evaluated based on precision, accuracy, recall, f1 score, and the Kappa statistics value. The results indicate that the eXtreme Gradient Boosting method is recommended for predicting children nutritional status with precision, accuracy, recall, f1 score, and kappa statistics values: 0.7812, 0.7995, 0.7799, 0.7884, and 0.6299, respectively. The features that determine the nutritional status of children with cardiac disease are ROSS/NYHA, types of cardiac disease, pulmonary hypertension, anemia, pneumonia, and residence. This research will assist policy makers and healthcare providers to develop a framework for implementing necessary interventions and care practices to prevent complications associated with nutritional status in cardiac patients.
VL  - 12
IS  - 3
ER  -

Copy | Download