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- Title
Machine learning algorithms in predicting the recurrence of renal stones using clinical data.
- Authors
Li, Pei; Li, Yang; Yang, Bowei; Zhang, Xingyu; Li, Jiongming
- Abstract
This document discusses the use of machine learning algorithms in predicting the recurrence of renal stones using clinical data. The study compares different algorithmic techniques, including support vector machine (SVM), random forest (RF), and logistic regression (GLM), to find the best model for predicting urolithiasis recurrence. The data set used in the study consisted of 401 clinical cases, and the RF model demonstrated the best performance in terms of internal validity. The study concludes that the RF model has better generalization ability compared to the other models. The authors declare no conflict of interests.
- Subjects
MACHINE learning; KIDNEY stones; SUPPORT vector machines; RANDOM forest algorithms; CANCER relapse; LOGISTIC regression analysis
- Publication
Urolithiasis, 2023, Vol 52, Issue 1, p1
- ISSN
2194-7228
- Publication type
Article
- DOI
10.1007/s00240-023-01516-5