Conference Abstract | Volume 8, Abstract NACNDC/19JASH019 (Poster B39) | Published: 20 Nov 2025
Noel Emma Esutu1,&, Irene Agaba2, Chris Anold Balwanaki1, Edith Nakku-Joloba1, Atugonza Gamukama1, Ruth Namazzi2, Sarah Kiguli2
1School of Public Health, Makerere University, Kampala, Uganda, 2Department of Paediatrics and Child Health, College of Health Sciences, Makerere University, Kampala, Uganda
&Corresponding author: Noel Emma Esutu, School of Public Health, Makerere University, Kampala, Uganda. Email: noelesutu@gmail.com, ORCID: https://orcid.org/0009-0003-3533-4932
Received: 11 Sept 2025, Accepted: 20 Oct 2025, Published: 20 Nov 2025
Domain: Nephrology
Keywords: Sickle cell disease, Pediatric nephrology, Machine learning, Explainable AI, Kidney, Dysfunction, Uganda
©Noel Emma Esutu et al Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article: Noel Emma Esutu et al. Developing a prediction model for early detection and classification of kidney dysfunction in children with sickle cell disease. Journal of Interventional Epidemiology and Public Health. 2025;8(ConfProc6):00019. https://doi.org/10.37432/JIEPH-CONFPRO6-00019
Children with sickle cell disease are at an elevated risk of developing chronic kidney disease, yet early detection remains a challenge in low-resource settings like Uganda. Traditional diagnostic tools often fail to capture subclinical kidney dysfunction, particularly in pediatric populations where glomerular hyperfiltration and atypical progression are common. Machine learning offers a promising complementary avenue for early risk identification, but model interpretability and contextual adaptation remain barriers to clinical integration. We developed a prediction model for the early detection and classification of kidney dysfunction in children aged 18 years and below with sickle cell disease at Mulago National Referral Hospital Uganda.
This retrospective study used data from pediatric SCD patients at Mulago National Referral Hospital and the Uganda Sickle Pan Africa Research Consortium (SPARCo) registry. Kidney dysfunction was staged according to KDIGO 2012 guidelines. A Random Forest classifier was trained on demographic, clinical, and laboratory features, with stratified 5-fold cross-validation.
A Random Forest–based model demonstrated high performance in detecting and classifying kidney dysfunction in pediatric SCD patients in Uganda. Integrating explainable AI with clinical staging enhances interpretability, offering a promising tool for early risk stratification and intervention in resource-limited settings.
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