Predicting symptomatic kidney stones using machine learning algorithms: insights from the Fasa adults cohort study (FACS)
ML models show promise in evaluating an individual's risk of developing painful kidney stones and recommending early lifestyle changes to reduce this risk. Further research can enhance predictive accuracy and tailor interventions for better prevention/management.
Authors
- Aref Andishgar (ORCID: https://orcid.org/0000-0002-8196-5300)
- Eisa Mahmoudi (ORCID: https://orcid.org/0000-0001-6109-7342)
- Alireza Monsef (ORCID: https://orcid.org/0000-0002-4623-2988)
- Reza Tabrizi (ORCID: https://orcid.org/0000-0001-7634-3948)
- Fatemeh Mahmoodi (ORCID: https://orcid.org/0000-0003-3595-6769)
- Sina Bazmi (ORCID: https://orcid.org/0000-0002-2128-0924)
Institutions
- Yazd University (IR)
- Universal Scientific Education and Research Network (IR)
- Fasa University of Medical Sciences (IR)
Publication Details
- Journal
- BMC Research Notes
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1186/s13104-024-06979-2
- Citations
- 7
- Primary Topic
- Kidney Stones and Urolithiasis Treatments
- Type
- article
- Field-Weighted Citation Impact
- 2.93
Funders
- Fasa University of Medical Sciences