Machine Learning Algorithms for Predicting Urinary Tract Infections - Integration of Demographic Data and Dipstick Reflectance Results - Evaluation and Results

Aug 3, 2026

Urinary tract infections are common in healthcare, yet standard diagnosis often takes 24–48 hours because urine cultures are required, while 70–80% of cultures are negative. This study develops and evaluates six machine learning models to predict UTIs and reduce unnecessary antibiotic use by flagging likely negative samples. Using urine data from 22,961 patients, models integrate demographic variables and dipstick reflectance results, assessing multiple UTI definitions with training and independent testing sets.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Background

Current diagnostic challenges for UTIs

Methods

Dataset and model evaluation approach

Predictive parameters and UTI definitions

Results

Best-performing model performance

Comparison with nitrite/leukocyte esterase

Conclusion

Clinical value and recommended next steps

Institutional Repository-Research Portal Dépôt Institutionnel-Portail de la Recherche University of easr[chportal.unamur.be](chportal.unamur.be) RESEARCH OUTPUTS / RÉSULTATS DE RECHERCHE Machine Learning Algorithms for Predicting Urinary Tract Infections Favresse, Julien; Cabo, Julien; Bosse, Maxime; Lardinois, Benjamin; Cadrobbi, Julie; Laffineur, Kim; Elsen, Marc; Douxfils, Jonathan; Roelandts, Liam; De Bruyne, Sander Published in: Clinical Chemistry DOI: 10.1093/clinchem/hvaf088 Publication date: 2025 Link to publication Citation for pulished version (HARVARD): Favresse, J, Cabo, J, Bosse, M, Lardinois, B, Cadrobbi, J, Laffineur, K, Elsen, M, Douxfils, J, Roelandts, L & De Bruyne, S 2025, 'Machine Learning Algorithms for Predicting Urinary Tract Infections: Integration of Demographic Data and Dipstick Reflectance Results', Clinical Chemistry, vol. 71, no. 10, pp. 1083-1094. [https://doi.org/10.1093/clinchem/hvaf088](https://doi.org/10.1093/clinchem/hvaf088) General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Download date: 16. Dec. 2025 Clinical Chemistry 00:0 Article 1–12 (2025) Machine Learning Algorithms for Predicting Urinary Tract Infections: Integration of Demographic Data and Dipstick Reflectance Results Julien Favresse,a, b,* Julien Cabo ,a Maxime Bosse,a Benjamin Lardinois,a Julie Cadrobbi,a Kim Laffineur,a Marc Elsen,a Jonathan Douxfils,b,c Liam Roelandts,a,c and Sander De Bruyned,e BACKGROUND: Urinary tract infections (UTIs) are among the most common infections encountered in healthcare settings. Current diagnostic practices often require 24–48 h due to the time needed for culture results. Given that 70%–80% of cultures return negative, there is significant interest in rapidly identifying negative samples to reduce unnecessary antibiotic use. This study aimed to develop and evaluate 6 machine learning models to predict UTIs. METHODS: Urine samples from 22 961 patients, collected between September 28, 2023 and June 29, 2024, were analyzed. Six machine learning models were assessed for their ability to predict UTIs based on 5 definitions incorporating pyuria and culture outcomes. The dataset was randomly divided into a training set (70%, n = 16 072) and an independent test set (30%, n = 6889) . Seventeen predictive parameters, including dipstick reflectance results and demographic variables, were evaluated. RESULTS: The CatBoost Classifier emerged as the bestperforming model, achieving an area under the ROC curve of 92.0%–94.7% depending on the UTI definition, with a negative predictive value consistently exceeding 95%, and an average precision ranging from 68.2% to 81.6% . In comparison, the predictive performance of nitrite and/or leukocyte esterase was significantly lower. a Department of Laboratory Medicine, Clinique St-Luc Bouge, Namur, Belgium; bFaculty of Medicine, Clinical Pharmacology and Toxicology Research Unit, Namur Research Institute for Life Sciences, University of Namur, Namur, Belgium; cResearch and Development Department, Qualiblood s. a., Namur, Belgium; dDepartment of Diagnostic Sciences, Ghent University, Ghent, Belgium; e Department of Laboratory Medicine, Jessa Hospital, Hasselt, Belgium. *Address

FAQs

Why is rapid UTI prediction clinically important?

Urine culture results often require 24–48 hours, and most cultures return negative, creating opportunities to reduce unnecessary antibiotic use through faster, more accurate prediction of likely negative cases.

How were the machine learning models evaluated in this study?

Urine samples from 22,961 patients were split into a training set (70%) and an independent test set (30%). Six models were assessed using multiple UTI definitions incorporating pyuria and culture outcomes, based on 17 predictive parameters including dipstick reflectance and demographic variables.

Which model performed best and how did it compare to traditional tests?

The CatBoost Classifier performed best, reaching an ROC area of 92.0%–94.7% depending on the UTI definition, with negative predictive value above 95%. Compared with nitrite and/or leukocyte esterase, its predictive performance was significantly higher.

Aug 3, 2026

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