ORIGINAL RESEARCH article
Front. Pediatr.
Sec. Pediatric Nephrology
This article is part of the Research TopicArtificial Intelligence-Driven Multi-Modal Sensing Technologies for Pediatric Health Monitoring and DiagnosticsView all articles
Integrating machine learning for advanced analysis of bioelectrical impedance parameters in children with nephrotic syndrome
Provisionally accepted
Josephine Reinert Quist1*
Leigh C Ward2
Lars Jødal3René Frydensbjerg Andersen4Christian Lodberg Hvas5Steven Brantlov4- 1Aarhus University Hospital, Aarhus, Denmark
- 2The University of Queensland School of Chemistry and Molecular Biosciences, Saint Lucia, Australia
- 3Aalborg Universitetshospital, Aalborg, Denmark
- 4Aarhus Universitetshospital, Aarhus, Denmark
- 5Aarhus Universitet Institut for Klinisk Medicin, Aarhus, Denmark
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Background: Nephrotic syndrome (NS) in children, entailing kidney-related protein leakage and peripheral oedema, remains difficult to assess. Bioelectrical impedance analysis (BIA) provides indices of body water (oedema), and analysis with machine learning (ML) may improve clinical care. We tested an ML model to identify NS in children, compared with healthy children. Methods: This was a cross-sectional study, conducted on children with active NS in the acute phase (aNS group) included from the Department of Paediatrics and Adolescent Medicine, Aar-hus University Hospital, Denmark. Anonymized MF-BIA data from frequences between 5-1000 kHz were added to a web-based ML platform, JustAddDataBio (JADBio)®, for analysing potential biomarkers to improve diagnosis. Results: Eight children with aNS and 38 healthy children of similar ages were included. The ML software employed a ridge logistic regression with the penalty hyperparameter lambda = 0.001, with a selected threshold of 0.81 by JADBio, and the area under the curve (AUC) in the best model was 0.84 [95% confidence interval (CI): 0.72;0.94]. The software selected the following features to include: height, age, resistance at 50 kHz, impedance at 50 kHz, the characteristic frequency, phase angle at 50 kHz and sex. The model had a statistically significant true positive classification of a healthy child of 0.92 (92%) [CI: 0.88;0.96], and a specificity of 0.22 (22%) [CI: 0.08;0.36]. Conclusion: Applying an ML-supported evaluation of BIA improved diagnostics. A low specificity limits the clinical application. To obtain a more acceptable model, a larger population of pa-tients and the inclusion of more biomarkers may be needed.
Keywords: bioelectrical impedance, Children, Electric Capacitance, machine learning, oedema
Received: 27 Sep 2025; Accepted: 14 Jan 2026.
Copyright: © 2026 Quist, Ward, Jødal, Andersen, Hvas and Brantlov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Josephine Reinert Quist
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