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

Chilyabanyama, Obvious Nchimunya; Chilengi, Roma; Simuyandi, Michelo; Chisenga, Caroline Cleopatra; Chirwa, Masuzyo; Hamusonde, Kalongo; Saroj, Rakesh Kumar; Iqbal, Najeeha Talat; Ngaruye, Innocent; Bosomprah, Samuel

doi:10.3390/children9071082

Cited by 25 publications

(9 citation statements)

References 32 publications

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“…They achieved the maximum accuracy of 87.7% for wasted, 88.3% for stunted, and 85.7% for underweight, obtained by the RF algorithm. Random Forest performed better than other algorithms in Chilyabanyama's research on predicting stunting among children under five in Zambia, which supports the current investigation's findings [49].…”

Section: Plos Onesupporting

confidence: 86%

Machine learning prediction of nutritional status among pregnant women in Bangladesh: Evidence from Bangladesh demographic and health survey 2017–18

Begum,

Rahman,

Omar Faruk

2024

PLoS ONE

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Aim Malnutrition in pregnant women significantly affects both mother and child health. This research aims to identify the best machine learning (ML) techniques for predicting the nutritional status of pregnant women in Bangladesh and detect the most essential features based on the best-performed algorithm. Methods This study used retrospective cross-sectional data from the Bangladeshi Demographic and Health Survey 2017–18. Different feature transformations and machine learning classifiers were applied to find the best transformation and classification model. Results This investigation found that robust scaling outperformed all feature transformation methods. The result shows that the Random Forest algorithm with robust scaling outperforms all other machine learning algorithms with 74.75% accuracy, 57.91% kappa statistics, 73.36% precision, 73.08% recall, and 73.09% f1 score. In addition, the Random Forest algorithm had the highest precision (76.76%) and f1 score (71.71%) for predicting the underweight class, as well as an expected precision of 82.01% and f1 score of 83.78% for the overweight/obese class when compared to other algorithms with a robust scaling method. The respondent’s age, wealth index, region, husband’s education level, husband’s age, and occupation were crucial features for predicting the nutritional status of pregnant women in Bangladesh. Conclusion The proposed classifier could help predict the expected outcome and reduce the burden of malnutrition among pregnant women in Bangladesh.

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Section: Plos Onesupporting

confidence: 86%

Machine learning prediction of nutritional status among pregnant women in Bangladesh: Evidence from Bangladesh demographic and health survey 2017–18

Begum,

Rahman,

Omar Faruk

2024

PLoS ONE

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“…SVM is a set of supervised learning methods used for classification, regression, and outlier detection. SVMs are preferred when dealing with high-dimensional spaces, robustness to outliers, nonlinearity, margin maximization, memory efficiency, and small to medium-sized datasets are important considerations for the problem at hand 42 . However, SVMs may have limitations in terms of scalability to large datasets and computational efficiency, especially when using non-linear kernels.…”

Section: Methodsmentioning

confidence: 99%

“…LR is a supervised ML algorithm used to solve classification issues. It is a parametric method that assumes a Bernoulli distribution of the target variable and the independence of the observations 42 .…”

Section: Methodsmentioning

confidence: 99%

Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia

Zemariam,

Yimer,

Abebe

et al. 2024

Sci Rep

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In developing countries, one-quarter of young women have suffered from anemia. However, the available studies in Ethiopia have been usually used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of anemia among youth girls in Ethiopia. A total of 5642 weighted samples of young girls from the 2016 Ethiopian Demographic and Health Survey dataset were utilized. The data underwent preprocessing, with 80% of the observations used for training the model and 20% for testing. Eight machine learning algorithms were employed to build and compare models. The model performance was assessed using evaluation metrics in Python software. Various data balancing techniques were applied, and the Boruta algorithm was used to select the most relevant features. Besides, association rule mining was conducted using the Apriori algorithm in R software. The random forest classifier with an AUC value of 82% outperformed in predicting anemia among all the tested classifiers. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having more than 5 family size were the top attributes to predict anemia. Association rule mining was identified the top seven best rules that most frequently associated with anemia. The random forest classifier is the best for predicting anemia. Therefore, making it potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt anemia among youth girls.

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“…For example, a study that evaluated the performance of machine learning classifiers in predicting stunting among children under five in Zambia. The study found that the random forest classifier outperformed other classifiers in terms of accuracy, sensitivity, and specificity (Chilyabanyama et al, 2022).…”

Section: A Introductionmentioning

confidence: 92%

CART Classification on Ordinal Scale Data with Unbalanced Proportions using Ensemble Bagging Approach

Arini,

Solimun,

Efendi

et al. 2024

JTAM

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CART is one of the algorithms in data exploration techniques with decision tree techniques. Unbalanced class proportions in the classification process can cause classification results of minor data to be incorrect. One way to overcome the problem of data imbalance is to use an ensemble bagging algorithm. The bagging algorithm utilizes the resampling method to carry out classification so that it can reduce bias in imbalanced data. The data used is secondary data from Fernandes and Solimun's 2023 research report. The number of sample are 100 respondents that has been valid and reliable. The sample for this research was mothers with toddlers in Wajak village, Malang Regency. The results showed that the ensemble bagging CART method is better at overcoming the problem of imbalance in the proportion of classes with a performance value of accuracy, sensitivity, specificity, and F1-Score values of 85%, 94.1%, 66.7%, and 78%. This research is limited to the Sumberputih Village area. So, the results of this research are only representative for the Wajak District area.

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

Cited by 25 publications

References 32 publications

Machine learning prediction of nutritional status among pregnant women in Bangladesh: Evidence from Bangladesh demographic and health survey 2017–18

Machine learning prediction of nutritional status among pregnant women in Bangladesh: Evidence from Bangladesh demographic and health survey 2017–18

Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia

CART Classification on Ordinal Scale Data with Unbalanced Proportions using Ensemble Bagging Approach

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