Using Machine Learning to Fight Child Acute Malnutrition and Predict Weight Gain During Outpatient Treatment with a Simplified Combined Protocol.
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...
| Publicado en: | Nutrients Vol. 16; no. 23; pp. 4213 - 4226 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
MDPI
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181658877&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181658877 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726643 B0TT jtl: Nutrients issn: 20726643 maglogo: N pubinfo: dt: Dec2024 vid: 16 iid: 23 pid: 97109 pub: MDPI artinfo: ui: 181658877 181658877 181658877 10.3390/nu16234213 181658877 ppf: 4213 ppct: 13 formats: tig: atl: Using Machine Learning to Fight Child Acute Malnutrition and Predict Weight Gain During Outpatient Treatment with a Simplified Combined Protocol. aug: au: Sánchez-Martínez, Luis Javier Charle-Cuéllar, Pilar Gado, Abdoul Aziz Ousmane, Nassirou Hernández, Candela Lucía López-Ejeda, Noemí affil: Unit of Physical Anthropology, Department of Biodiversity, Ecology and Evolution, Faculty of Biological Sciences, Complutense University of Madrid, 28040 Madrid, Spain sug: subj: Machine Learning Utilization Acute Disease Diet Therapy Malnutrition Diet Therapy Weight Gain Prevention and Control Ambulatory Care Wasting Syndrome Diet Therapy Socioeconomic Factors Child, Hospitalized Niger Human Funding Source Infant Child, Preschool Outpatients Niger Prospective Studies Algorithms Wasting Syndrome Child Nutrition Disorders Diet Therapy Child Nutrition Disorders Prevention and Control Patient Discharge Public Health Random Forest Caregivers Psychosocial Factors Health Services Accessibility World Health Organization Descriptive Statistics ROC Curve Infant: 1-23 months Child, Preschool: 2-5 years 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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