Machine Learning-Based Prediction of Urolithiasis Recurrence Using Patient's Clinical Data, Demography, and CT Findings.
This study presents a practical application of predictive machine learning methods for predicting urolithiasis recurrence with clinically acceptable accuracy compared to traditional scoring systems. To select the best classifier, six different predictive ML models have been evaluated using different performance metrics and analysis tools.
Authors
- Abdolreza Mohammadi (ORCID: https://orcid.org/0000-0002-0483-2635)
- Seyed Jalaleddin Mousavirad (ORCID: https://orcid.org/0000-0001-8661-7578)
- Iman Menbari Oskouie (ORCID: https://orcid.org/0000-0002-5511-1512)
- Razman Arabzadeh Bahri (ORCID: https://orcid.org/0000-0002-9582-5798)
- Hassan Homayoun
- Seyed Mohammad Kazem Aghamir (ORCID: https://orcid.org/0000-0003-1611-0362)
- Leila Zareian Baghdadabad
Institutions
- Mid Sweden University (SE)
- Tehran University of Medical Sciences (IR)
Publication Details
- Journal
- PubMed
- Published
- 2025-11-12
- DOI
- https://doi.org/10.22037/uj.v22i.8544
- Primary Topic
- Kidney Stones and Urolithiasis Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00