A Machine Learning Approach to Care Optimization for UTI Patients: A Case Study of the Tamale Teaching Hospital, Northern Region, Ghana

Background: Urinary tract infections (UTIs) are a common clinical concern with significant public health implications. While sociodemographic factors are believed to influence UTI risk, their predictive value remains inconsistent across populations. This study aimed to identify sociodemographic predictors of UTI status using both traditional statistical methods and machine learning (ML) models. Methods: A cross-sectional analysis was conducted in the northern region of Ghana on a sample of 2,598 individuals. Descriptive statistics and chi-square tests were used to examine associations between UTI status and sociodemographic variables. Five ML classification models-boosting, random forest, decision tree, k-nearest neighbours, and support vector machine-were trained and evaluated using accuracy, precision, recall, and F1 score. Data were split into training (64%), validation (16%), and test (20%) sets. Results: Age and gender were significantly associated with UTI status (p