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BioMedInformatics
Volume 5
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10.3390/biomedinformatics5040057
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Development of a Machine Learning-Based Predictive Model for Urinary Tract Infection Risk in Patients with Vitamin D Deficiency: A Multidimensional Clinical Data Analysis

by
Krittin Naravejsakul
1,
Watcharaporn Cholamjiak
2,
Watcharapon Yajai
2,
Jakkaphong Inpun
3 and
Waragunt Waratamrongpatai
1,*
1
School of Medicine, University of Phayao, Phayao 56000, Thailand
2
Department of Mathematics, School of Science, University of Phayao, Phayao 56000, Thailand
3
School of Information and Communication Technology, University of Phayao, Phayao 56000, Thailand
*
Author to whom correspondence should be addressed.
BioMedInformatics 2025, 5(4), 57; https://doi.org/10.3390/biomedinformatics5040057
Submission received: 7 August 2025 / Revised: 3 October 2025 / Accepted: 9 October 2025 / Published: 10 October 2025
(This article belongs to the Special Issue Editor's Choices Series for Clinical Informatics Section)
Versions Notes

Abstract

Background: Urinary tract infections (UTIs) remain among the most common bacterial infections, yet reliable risk stratification remains challenging. Serum vitamin D has been linked to immune regulation, but its predictive role in UTI subtypes is unclear. Methods: We analyzed 332 de-identified clinical records using six machine learning algorithms: Extra Trees, Gradient Boosting, XGBoost, Logistic Regression, Random Forest, and LightGBM. Two preprocessing strategies were applied: (i) removing rows with missing fasting blood sugar (FBs) and HbA1c, and (ii) dropping columns with Null FBs and HbA1c values. Model performance was evaluated using 10-fold cross-validation. Results: Serum vitamin D showed weak correlations with UTI subtypes but modest importance in tree-based models. The highest predictive accuracy was obtained with Extra Trees (0.9510) under the row-removal strategy and Random Forest (0.9525) under the column-dropping strategy. Models excluding vitamin D maintained comparable accuracy, suggesting minimal impact on overall predictive performance. Conclusions: Machine learning models demonstrated high accuracy and robustness in predicting UTI subtypes across preprocessing strategies. While vitamin D contributes as a supportive feature, it is not essential for reliable prediction. These findings highlight the adaptability and clinical utility of both vitamin D-inclusive and vitamin D-exclusive models, supporting deployment in diverse healthcare settings.
Keywords: UTI; serum vitamin D; machine learning; clinical data analysis UTI; serum vitamin D; machine learning; clinical data analysis

Share and Cite

MDPI and ACS Style

Naravejsakul, K.; Cholamjiak, W.; Yajai, W.; Inpun, J.; Waratamrongpatai, W. Development of a Machine Learning-Based Predictive Model for Urinary Tract Infection Risk in Patients with Vitamin D Deficiency: A Multidimensional Clinical Data Analysis. BioMedInformatics 2025, 5, 57. https://doi.org/10.3390/biomedinformatics5040057

AMA Style

Naravejsakul K, Cholamjiak W, Yajai W, Inpun J, Waratamrongpatai W. Development of a Machine Learning-Based Predictive Model for Urinary Tract Infection Risk in Patients with Vitamin D Deficiency: A Multidimensional Clinical Data Analysis. BioMedInformatics. 2025; 5(4):57. https://doi.org/10.3390/biomedinformatics5040057

Chicago/Turabian Style

Naravejsakul, Krittin, Watcharaporn Cholamjiak, Watcharapon Yajai, Jakkaphong Inpun, and Waragunt Waratamrongpatai. 2025. "Development of a Machine Learning-Based Predictive Model for Urinary Tract Infection Risk in Patients with Vitamin D Deficiency: A Multidimensional Clinical Data Analysis" BioMedInformatics 5, no. 4: 57. https://doi.org/10.3390/biomedinformatics5040057

APA Style

Naravejsakul, K., Cholamjiak, W., Yajai, W., Inpun, J., & Waratamrongpatai, W. (2025). Development of a Machine Learning-Based Predictive Model for Urinary Tract Infection Risk in Patients with Vitamin D Deficiency: A Multidimensional Clinical Data Analysis. BioMedInformatics, 5(4), 57. https://doi.org/10.3390/biomedinformatics5040057

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