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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Yan, Yongchao Sun, Qihang Du, Haotian Sun, Wenming Guo, Yize Li, Bin Wang, Xinning |
| Abstract | Background Chronic Kidney Disease (CKD) is a common severe complication after radical nephrectomy in patients with renal cancer. The timely and accurate prediction of the long-term progression of renal function post-surgery is crucial for early intervention and ultimately improving patient survival rates. Objective This study aimed to establish a machine learning model to predict the likelihood of long-term renal dysfunction progression after surgery by analyzing patients’ general information in depth. Methods We retrospectively collected data of eligible patients from the Affiliated Hospital of Qingdao University. The primary outcome was upgrading of the Chronic Kidney Disease stage between pre- and 3-year post-surgery. We constructed seven different machine-learning models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (Lightgbm), Gaussian Naive Bayes (GaussianNB), and K-Nearest Neighbors (KNN). The performance of all predictive models was evaluated using the area under the receiver operating characteristic curve (AUC), precision-recall curves, confusion matrices, and calibration curves. Results Among 360 patients with renal cancer who underwent radical nephrectomy included in this study, 185 (51.3%) experienced an upgrade in Chronic Kidney Disease stage 3-year post-surgery. Eleven predictive variables were selected for further construction of the machine learning models. The logistic regression model provided the most accurate prediction, with the highest AUC (0.8154) and an accuracy of 0.787. |
| Related Links | https://bmcnephrol.biomedcentral.com/counter/pdf/10.1186/s12882-024-03907-1.pdf |
| Ending Page | 11 |
| Page Count | 11 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712369 |
| DOI | 10.1186/s12882-024-03907-1 |
| Journal | BMC Nephrology |
| Issue Number | 1 |
| Volume Number | 25 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-12-18 |
| Access Restriction | Open |
| Subject Keyword | Nephrology Internal Medicine Radical nephrectomy Chronic kidney disease Machine learning Kidney cancer Early diagnosis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Nephrology |
| Journal Impact Factor | 2.2/2023 |
| 5-Year Journal Impact Factor | 2.6/2023 |
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