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

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.