Automated Quantification of Vesicoureteral Reflux using Machine Learning with Advancing Diagnostic Precision

Mohammad Al-batah, Mohammad Al-Batah, Mowafaq Salem Alzboon and Esra Alzaghoul

Data and Metadata, 2025, vol. 4, 460

Abstract: This article uses machine learning to quantify vesicoureteral reflux (VUR). VCUGs in pediatric urology are used to diagnose VUR. The goal is to increase diagnostic precision. Various machine learning models categorize VUR grades (Grade 1 to Grade 5) and are evaluated using performance metrics and confusion matrices. Study datasets come from internet repositories with repository names and accession numbers. Machine learning models performed well across several measures. KNN, Random Forest, AdaBoost, and CN2 Rule Induction consistently scored 100% in AUC, CA, F1-score, precision, recall, MCC, and specificity. These models classified grades well individually and collectively. In contrast, the Constant model performed poorly across all criteria, suggesting its inability to categorize VUR grades reliably. With the most excellent average performance ratings, the CN2 Rule Induction model excelled at grade categorization. Confusion matrices demonstrate that machine learning models predict VUR grades. The large diagonal numbers of the matrices show that the models are regularly predicted effectively. However, the Constant model's constant Grade 5 forecast reduced its differentiation. This study shows that most machine learning methods automate VUR measurement. The findings aid objective pediatric urology grading and radiographic evaluation. The CN2 Rule Induction model accurately classifies VUR grades. Machine learning-based diagnostic techniques may increase diagnostic precision, clinical decision-making, and patient outcomes.

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:4:y:2025:i::p:460:id:1056294dm2025460

DOI: 10.56294/dm2025460

Access Statistics for this article

More articles in Data and Metadata from AG Editor
Bibliographic data for series maintained by Javier Gonzalez-Argote ().