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September 10, 2025Journal of Research in UrologyOpen Access

Prediction of Urinary Tract Infection for Hospital-admitted Patients based on Demographic and Historical Data, as well as Machine Learning Approaches

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Overview

Retrospective cohort study assesses machine learning methods for urinary tract infection prediction, indicating demographic data influences outcomes.

Key Points

  • Prediction of urinary tract infection using linear discriminant analysis achieved an accuracy of 65.16%.
  • Among age groups, accuracy rates for urinary tract infection prediction were highest in adults at 86.25%.
  • Machine learning algorithms were evaluated based on historical and demographic features for urinary tract infection prediction.
  • The study suggests improving prediction accuracy by incorporating additional accessible demographic features.

Cite This Study

A 2024 study studied this question.

synapsesocial.com/papers/68c1dda954b1d3bfb60fc91chttps://doi.org/10.32592/jru.8.1.18
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine Learning Algorithms for Predicting Urinary Tract Infections: Integration of Demographic Data and Dipstick Reflectance Results2025 · 6 citations
  2. 2Survey on Machine Learning Models to Analyze Urinary Tract Infection Data2024 · 1 citations
  3. 3Machine learning to predict bacteriuria in the emergency department2025
  4. 4From Traditional Statistics to Artificial Intelligence: Advancing Pediatric UTI Recurrence Prediction in Low-Resource Communities2025
  5. 5Interpretable Machine Learning Models for Predicting Critical Outcomes in Patients with Suspected Urinary Tract Infection with Positive Urine Culture2024 · 16 citations