Author:
Lee Yeonhee,Ryu Jiwon,Kang Min Woo,Seo Kyung Ha,Kim Jayoun,Suh Jungyo,Kim Yong Chul,Kim Dong Ki,Oh Kook-Hwan,Joo Kwon Wook,Kim Yon Su,Jeong Chang Wook,Lee Sang Chul,Kwak Cheol,Kim Sejoong,Han Seung Seok
Abstract
AbstractThe precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783–0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.
Publisher
Springer Science and Business Media LLC
Cited by
16 articles.
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