Su Chuan-Tsung, Chang Yi-Ping, Ku Yuh-Ting, Lin Chih-Ming
Department of Healthcare Information and Management, Ming Chuan University, Taoyuan 333, Taiwan.
Department of Nephrology, Taoyuan Branch of Taipei Veterans General Hospital, Taoyuan 330, Taiwan.
Diagnostics (Basel). 2022 Oct 11;12(10):2454. doi: 10.3390/diagnostics12102454.
This study assessed the feasibility of five separate machine learning (ML) classifiers for predicting disease progression in patients with pre-dialysis chronic kidney disease (CKD). The study enrolled 858 patients with CKD treated at a veteran's hospital in Taiwan. After classification into early and advanced stages, patient demographics and laboratory data were processed and used to predict progression to renal failure and important features for optimal prediction were identified. The random forest (RF) classifier with synthetic minority over-sampling technique (SMOTE) had the best predictive performances among patients with early-stage CKD who progressed within 3 and 5 years and among patients with advanced-stage CKD who progressed within 1 and 3 years. Important features identified for predicting progression from early- and advanced-stage CKD were urine creatinine and serum creatinine levels, respectively. The RF classifier demonstrated the optimal performance, with an area under the receiver operating characteristic curve values of 0.96 for predicting progression within 5 years in patients with early-stage CKD and 0.97 for predicting progression within 1 year in patients with advanced-stage CKD. The proposed method resulted in the optimal prediction of CKD progression, especially within 1 year of advanced-stage CKD. These results will be useful for predicting prognosis among patients with CKD.
本研究评估了五种不同的机器学习（ML）分类器预测透析前慢性肾脏病（CKD）患者疾病进展的可行性。该研究纳入了在台湾一家退伍军人医院接受治疗的858例CKD患者。在将患者分为早期和晚期阶段后，对患者的人口统计学和实验室数据进行处理，并用于预测肾衰竭的进展情况，同时确定了实现最佳预测的重要特征。采用合成少数过采样技术（SMOTE）的随机森林（RF）分类器在3年和5年内病情进展的早期CKD患者以及1年和3年内病情进展的晚期CKD患者中具有最佳的预测性能。预测早期和晚期CKD进展的重要特征分别是尿肌酐和血清肌酐水平。RF分类器表现出最佳性能，在预测早期CKD患者5年内病情进展时，受试者工作特征曲线下面积值为0.96；在预测晚期CKD患者1年内病情进展时，该值为0.97。所提出的方法实现了对CKD进展的最佳预测，尤其是在晚期CKD的1年内。这些结果将有助于预测CKD患者的预后。
