A typescript React library for displaying RCPCH Digital Growth Charts from API data
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Updated
Apr 14, 2026 - TypeScript
A typescript React library for displaying RCPCH Digital Growth Charts from API data
Data and R code to reproduce analyses examining socio-economic and environmental determinants of child-health outcomes among African nations
Sistem Klasifikasi Status Stunting menggunakan Machine Learning untuk mendeteksi status gizi balita berdasarkan BB/U, TB/U, dan BB/TB. Dibangun dengan Python, Flask, dan Scikit-learn.
Plot child growth charts against WHO standards
ML pipeline predicting child malnutrition risk in Chad using DHS 2014 survey data. Gradient Boosting achieved 92% accuracy and 0.979 AUC on 9,826 children. 52.9% of Chadian children under five are malnourished.
Predict child malnutrition risk in Chad with machine learning to help health workers act early and target care
Australia SA3-level analysis of relationship between child mortality/morbidity and climate conditions/air pollution
SPREADSHEET VISUALIZATION DASHBOARD OF MATERNAL AND CHILD HEALTH IN JEMBER REGENCY, 2021
"Born Too Soon? The Educational Costs of Early Elective Deliveries" by Libertad González (Universitat Pompeu Fabra & Barcelona School of Economics) & Parijat Maitra (Stockholm University)
Determining the most important predictors of diarrhoea in children under five in South and Southeast Asia by exploring the spatiotemporal association between diarrhoeal incidence and various behavioural, socio-demographic, and environmental factors.
Australia SA3-level analysis of relationship between child mortality/morbidity and climate conditions/air pollution
Maternal Health Learning Content based on the Africa CDC Knowledge Management platform
Open-source facility-level climate & health vulnerability scoring for schools and clinics, anywhere in the world.
Analysis of the prevalence of congenital anomalies in Brazil and its potential association with socioeconomic, regional and gestational factors from 2011 to 2020.
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