Publication: Enhancing UTI Severity Classification in Elderly Inpatients with AI: Toward Smarter Clinical Decision-Making
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Issued Date
2025-01-01
Resource Type
eISSN
27324494
Scopus ID
2-s2.0-105016318275
Journal Title
Iet Conference Proceedings
Volume
2025
Issue
15
Start Page
576
End Page
578
Rights Holder(s)
SCOPUS
Bibliographic Citation
Iet Conference Proceedings Vol.2025 No.15 (2025) , 576-578
Suggested Citation
Sukhachewanon J., Thongmeesee S., Chanwimalueang T., Tantisatirapong S. Enhancing UTI Severity Classification in Elderly Inpatients with AI: Toward Smarter Clinical Decision-Making. Iet Conference Proceedings Vol.2025 No.15 (2025) , 576-578. 578. doi:10.1049/icp.2025.2622 Retrieved from: https://hdl.handle.net/20.500.14740/50528
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Abstract
This study presents a machine learning-based system to assess mortality risk in elderly UTI patients using data from Chonburi Hospital, Thailand (2019-2023), involving 17 predictive variables. Following data preprocessing, which included addressing missing values, removing outliers, and standardizing data, key features were identified using the ReliefF algorithm. In this study, various classification models, including SVM, K-NN, and neural networks, were evaluated. The K-NN model demonstrated superior performance, a sensitivity of 96.3%, specificity of 81.6%, F1-score of 89.6%, and an overall accuracy of 92.2%. These results highlight the potential of the K-NN model for early risk assessment and its utility in enhancing clinical decision-making processes.