RT Journal Article
T1 Using Machine Learning to Fight Child Acute Malnutrition and Predict Weight Gain During Outpatient Treatment with a Simplified Combined Protocol
A1 Sánchez Martínez, Luis Javier
A1 Charle-Cuéllar, Pilar
A1 Gado, Abdoul Aziz
A1 Ousmane, Nassirou
A1 Hernández, Candela Lucía
A1 López Ejeda, Noemí
AB Background/Objectives: Child acute malnutrition is a global public health problem, affecting 45 million children under 5 years of age. The World Health Organization recommends monitoring weight gain weekly as an indicator of the correct treatment. However, simplified protocols that do not record the weight and base diagnosis and follow-up in arm circumference at discharge are being tested in emergency settings. The present study aims to use machine learning techniques to predict weight gain based on the socio-economic characteristics at admission for the children treated under a simplified protocol in the Diffa region of Niger. Methods: The sample consists of 535 children aged 6–59 months receiving outpatient treatment for acute malnutrition, for whom information on 51 socio-economic variables was collected. First, the Variable Selection Using Random Forest (VSURF) algorithm was used to select the variables associated with weight gain. Subsequently, the dataset was partitioned into training/testing, and an ensemble model was adjusted using five algorithms for prediction, which were combined using a Random Forest meta-algorithm. Afterward, Receiver Operating Characteristic (ROC) curves were used to identify the optimal cut-off point for predicting the group of individuals most vulnerable to developing low weight gain. Results: The critical variables that influence weight gain are water, hygiene and sanitation, the caregiver’s employment–socio-economic level and access to treatment. The final ensemble prediction model achieved a better fit (R2 = 0.55) with respect to the individual algorithms (R2 = 0.14–0.27). An optimal cut-off point was identified to establish low weight gain, with an Area Under the Curve (AUC) of 0.777 at a value of <6.5 g/kg/day. The ensemble model achieved a success rate of 84% (78/93) at the identification of individuals below <6.5 g/kg/day in the test set. Conclusions: The results highlight the importance of adapting the cut-off points for weight gain to each context, as well as the practical usefulness that these techniques can have in optimizing and adapting to the treatment in humanitarian settings.
PB MDPI
YR 2024
FD 2024-12-06
LK https://hdl.handle.net/20.500.14352/117995
UL https://hdl.handle.net/20.500.14352/117995
LA eng
NO Sánchez-Martínez, L. J., Charle-Cuéllar, P., Gado, A. A., Ousmane, N., Hernández, C. L., & López-Ejeda, N. (2024). Using machine learning to fight child acute malnutrition and predict weight gain during outpatient treatment with a simplified combined protocol. Nutrients, 16(23), 4213. https://doi.org/10.3390/nu16234213
NO This research project was funded by Elrha’s Research for Health in Humanitarian Crisis (R2HC) program [ref #40410] and The United States Agency for International Development (USAID) [award No. 720FDA19GR0029]. R2HC aims to improve health outcomes for people affected by crises by strengthening the evidence base for public health interventions. The R2HC program is funded by the UK Foreign, Commonwealth and Development Office (FCDO); the Wellcome; and the UK National Institute for Health Research (NIHR). L.J.S.-M. was granted a predoctoral fellowship from Complutense University and Banco Santander [CT58/21].
NO Elrha’s Research for Health in Humanitarian Crisis (R2HC)
NO United States Agency for International Development (USAID)
NO UK Foreign, Commonwealth and Development Office (FCDO)
NO UK National Institute for Health Research (NIHR)
NO Universidad Complutense de Madrid
NO Banco Santander
DS Docta Complutense
RD 8 abr 2025