Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia

Bitew, Fikrewold; Sparks, Corey S.; Nyarko, Samuel H.

doi:10.1017/s1368980021004262

Cited by 32 publications

(39 citation statements)

References 48 publications

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“…Despite Random Forest and XG Boost performing better than the traditional logistic regression, logistic regression still retains interpretability as the main advantage it has over the other ML algorithm. Similar studies used ML algorithms to predict the nutritional status of children using demographic health survey data [ 18 , 19 , 20 , 32 ]. Our results are similar to the findings of [ 19 ], which implicate the RF algorithm to be a superior predictor of stunting.…”

Section: Discussionmentioning

confidence: 99%

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Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia

et al. 2022

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Stunting is a global public health issue. We sought to train and evaluate machine learning (ML) classification algorithms on the Zambia Demographic Health Survey (ZDHS) dataset to predict stunting among children under the age of five in Zambia. We applied Logistic regression (LR), Random Forest (RF), SV classification (SVC), XG Boost (XgB) and Naïve Bayes (NB) algorithms to predict the probability of stunting among children under five years of age, on the 2018 ZDHS dataset. We calibrated predicted probabilities and plotted the calibration curves to compare model performance. We computed accuracy, recall, precision and F1 for each machine learning algorithm. About 2327 (34.2%) children were stunted. Thirteen of fifty-eight features were selected for inclusion in the model using random forest. Calibrating the predicted probabilities improved the performance of machine learning algorithms when evaluated using calibration curves. RF was the most accurate algorithm, with an accuracy score of 79% in the testing and 61.6% in the training data while Naïve Bayesian was the worst performing algorithm for predicting stunting among children under five in Zambia using the 2018 ZDHS dataset. ML models aids quick diagnosis of stunting and the timely development of interventions aimed at preventing stunting.

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Section: Discussionmentioning

confidence: 99%

“…These methods have been applied in predicting malnutrition using different datasets [ 16 , 17 , 18 , 19 ]. Furthermore, machine learning methods have been shown to be superior to classical statistical methods when solving classification problems [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia

et al. 2022

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“…The authors however suggest that Anaemia and VAD are important public health problems among the tribal population of North-East India, despite their rich biodiversity. Bezboruah et al ( 2021 ) conducted a cross-sectional study of 104 HIV-positive children in one of the tertiary care centers in North-East India. According to their study, compared to the older age group preschool children had a higher prevalence of anaemia.…”

Section: Introductionmentioning

confidence: 99%

Predicting child anaemia in the North-Eastern states of India: a machine learning approach

Meitei

Saini

Mohapatra

et al. 2022

Int J Syst Assur Eng Manag

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Child anaemia is a serious global health issue and India is one of the highest contributors among the developing nations. Researchers identify many harmful effects of anaemia, which include psychomotor retardation, which in turn decreases the learning ability and causes low intelligence among pre-school children. The effects also include behavioural delays, low immunity, and susceptibility to frequent infections, increased mortality, and disability. The present study aims to predict anaemia among children in North-East India by applying Machine Learning (ML) algorithms to latest available National Family Health Survey (NFHS)-4 data. Out of the total 29,312 eligible children (6–59 months) in North-East India, a total of 21,000 children with demographic variables without any missing observations, wherein 10,460 are anaemic, is considered for this study. Machine learning (ML) algorithms have been applied through 3 different types of penalized regression methods—ridge, least absolute shrinkage and selection operator, and elastic net for predicting anaemia. A systematic assessment of algorithms is performed in terms of accuracy, sensitivity, specificity, F1-Score, and Cohen’s -Statistics. Having achieved the receiver operating characteristic value of over 70% in training and accuracy of above 64% while testing, it can be safely asserted that factors like mother’s anaemic status, age of the child, social status, mother’s age, mother’s education, religion are important in identifying the child as anaemic.

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“…Stunting problems in the short and long term in the process of child development, such as the occurrence of decline in cognitive function and other dangerous diseases, stunting problems need to be a priority, especially in the field of health [6]. Malnutrition is a serious problem in the global community resulting in chronic disease and death [7]. Malnutrition affects decreased muscle function, immune disorders, and brain dysfunction and can cause disorders of nerve development [8].…”

Section: Introductionmentioning

confidence: 99%

“…This study showed that of 107 children who were followed up to 48 months of age, 51% experienced stunting height-for-age Z-score (HAZ<-2) at birth which increased to 54% at 48 months of age. Research by Bitew et al [7] use algorithms on machine learning to predict toddlers' malnutrition in Ethiopia. Descriptive results show that the xgbTree algorithm has better predictive capabilities than common linear mixed algorithms.…”

Section: Introductionmentioning

confidence: 99%

Prediction of linear model on stunting prevalence with machine learning approach

Mambang

Marleny²,

Zulfadhilah³

2023

Bulletin EEI

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An increase in the number of residents should be anticipated including in the health sector, especially the problem of stunting. Stunting in children disrupts height and lack of absorption of nutrients. Information and data drive change in many areas such as health, entertainment, economics, business, and other strategic areas. The stages carried out in this study are initiating, developing linear models, and making prediction results on linear machine learning models. The results of testing with the scikit-learn linear model with a minimum variable of 19 get the best test results, namely the polynomial regression with pipeline model with mean absolute percentage error (MAPE) 0.02, root mean square error (RMSE) 3.32, and coefficient of determination (R2) 1,00. Testing with the scikit-learn linear model with a maximum variable of 48 gets the best test results, namely the polynomial regression with pipeline model with MAPE 0.00, RMSE 3.79 and R2 1.00. Testing with the scikit-learn linear model with an average variable of 32 gets the best test results, namely the polynomial regression model with MAPE 0.01, RMSE 3.32, and R2 1.00. The results of testing with the scikit-learn linear model with the minimum, maximum, and average variables get the best test results, namely the polynomial regression with pipeline model.

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Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia

Cited by 32 publications

References 48 publications

Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia

Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia

Predicting child anaemia in the North-Eastern states of India: a machine learning approach

Prediction of linear model on stunting prevalence with machine learning approach

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