Efficient Machine Learning for Malnutrition Prediction among under-five children in India

Jain, Saksham; Khanam, Tayyibah; Abedi, Ali Jafar; Khan, Abid Ali

doi:10.1109/delcon54057.2022.9753080

Cited by 9 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar study in Ethiopia using the XGBoost algorithm identified children aged > 30 months, wealth index (poorest), time to water intake, ethnicity (Somalia), and small child size as the five main features associated with wasting [ 1 ]. Another study in India identified mother’s BMI, toilet facility, state, and child’s age and religion as important features of wasting [ 14 ]. The age of the child is an important factor to consider with respect to wasting among children.…”

Section: Discussionmentioning

confidence: 99%

“…A previous study from Ethiopia reported the time to water, child age greater than 30 months, number of children under five in a household, household possession of television, and small child size [ 1 ]. Another study from India reported that children’s age, toilet facilities, wealth index, mother’s education, and breastfeeding duration were important features associated with stunting [ 14 ]. Common features identified across studies were weight of the child, age of the child, sex of child, region of residence, and ethnicity.…”

Section: Discussionmentioning

confidence: 99%

“…Both the SVM and XGBoost models recorded the highest accuracy and showed an optimal balance of sensitivity and specificity to underweight prediction. Previous studies have reported that the accuracy of ML algorithms in predicting undernutrition is between 35.6% and 99.95% [1,6,[12][13][14].…”

Section: Plos Onementioning

confidence: 99%

“…Machine learning approaches have shown promising outcomes in identifying factors associated with undernutrition, including previously undiscovered factors [ 1 ]. Several studies have been conducted in Bangladesh [ 10 , 12 , 13 ], Ethiopia [ 1 , 6 ], and India [ 14 , 15 ] and have shown the usefulness of machine learning algorithms. Factors predicted to be significantly associated with undernutrition vary based on location, including time to water source, anaemia history, child age > 30 months, low birth weight and maternal underweight [ 1 ], urban-rural settlement, literacy factors of parents, and place of residence [ 6 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting and identifying factors associated with undernutrition among children under five years in Ghana using machine learning algorithms

Anku,

Duah

2024

PLoS ONE

View full text Add to dashboard Cite

Background Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Methods Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). Results In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. Conclusion The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting and identifying factors associated with undernutrition among children under five years in Ghana using machine learning algorithms

Anku,

Duah

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Dietary deficiency is one of the major bottlenecks in improving economic and social growth. According to the World Health Organization (WHO), millions of children are suffering from stunting, wasting and overweight and the most prominent countries where highest degree of malnourished people were found are Asia and Africa (Central Sub-Saharan Africa) [1][2]. According to the Joint Child Malnutrition Estimates (JME) provided by the World Health Organization (WHO)…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Malnutrition Detection

Ankalaki,

G Biradar,

Naik P

et al. 2024

Int. J. Onl. Eng.

View full text Add to dashboard Cite

The timely detection of malnutrition in children is of paramount importance, as it allows for early intervention and treatment. This proactive approach not only prevents further health deterioration but also fosters proper growth, minimizing the long-term consequences of malnutrition, such as stunted growth, impaired cognitive development, and increased vulnerability to diseases. Our work encompasses the creation of a new dataset comprising images of children in Healthy, Undernourished, Stunting, and Wasting categories. The core objective is to assess the deep learning model performance in classifying these children images. The experimentation is carried out by varying epochs, batch size, optimizers AdamW, Adamax, and RMSprop; and different values of the learning rate 0.1, 0.01, 0.001, and 0.0001 during model training. The model is trained on image dataset constructed by cleaning images generated by the stable diffusion model. The model is tested on randomly selected child images from websites. The model successfully classified two classes with 95% accuracy, 97.6% F1 score, precision 97.6%, and 97.6% recall with Adam optimizers, 0.0001 learning rate, and Batch size 4. Additionally, for the four-class categorization scenario, the study broadens the classification. The model achieved 88.87% accuracy, 90.3% recall, 90.2% precision, and an F1 score of 90% for four-class categorization with AdamW optimization, 0.0001 learning rate, and batch size 6. These results are satisfactory for prediction of malnutrition category in children.

show abstract

Detection of Malnutrition in Children Using Deep Learning Model

Biradar,

Naik

2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Efficient Machine Learning for Malnutrition Prediction among under-five children in India

Cited by 9 publications

References 7 publications

Predicting and identifying factors associated with undernutrition among children under five years in Ghana using machine learning algorithms

Predicting and identifying factors associated with undernutrition among children under five years in Ghana using machine learning algorithms

A Deep Learning Approach for Malnutrition Detection

Detection of Malnutrition in Children Using Deep Learning Model

Contact Info

Product

Resources

About