期刊論文

學年 111
學期 1
出版(發表)日期 2022-12-09
作品名稱 A Hybrid risk factor evaluation scheme for metabolic syndrome and stage 3 Chronic Kidney Disease based on multiple machine learning techniques.
作品名稱(其他語言)
著者 Mao-Jhen Jhou; Ming-Shu Chen; Tian-Shyug Lee; Chih-Te Yang; Yen-Ling Chiu; Chi-Jie Lu*
單位
出版者
著錄名稱、卷期、頁數 Healthcare 10(12), 2496
摘要 With the rapid development of medicine and technology, machine learning (ML) techniques are extensively applied to medical informatics and the suboptimal health field to identify critical predictor variables and risk factors. Metabolic syndrome (MetS) and chronic kidney disease (CKD) are important risk factors for many comorbidities and complications. Existing studies that utilize different statistical or ML algorithms to perform CKD data analysis mostly analyze the early-stage subjects directly, but few studies have discussed the predictive models and important risk factors for the stage-III CKD high-risk health screening population. The middle stages 3a and 3b of CKD indicate moderate renal failure. This study aims to construct an effective hybrid important risk factor evaluation scheme for subjects with MetS and CKD stages III based on ML predictive models. The six well-known ML techniques, namely random forest (RF), logistic regression (LGR), multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost), gradient boosting with categorical features support (CatBoost), and a light gradient boosting machine (LightGBM), were used in the proposed scheme. The data were sourced from the Taiwan health xamination indicators and the questionnaire responses of 71,108 members between 2005 and 2017. In total, 375 stage 3a CKD and 50 CKD stage 3b CKD patients were enrolled, and 33 different variables were used to evaluate potential risk factors. Based on the results, the top five important variables, namely BUN, SBP, Right Intraocular Pressure (R-IOP), RBCs, and T-Cho/HDL-C (C/H), were identified as significant variables for evaluating the subjects with MetS and CKD stage 3a or 3b.
關鍵字 machine learning (ML);Metabolic syndrome (MetS);chronic kidney disease (CKD);end-stage kidney disease (ESKD);hybrid risk factor
語言 en_US
ISSN 2227-9032
期刊性質 國外
收錄於 SSCI Scopus
產學合作
通訊作者 Chi-Jie Lu
審稿制度
國別 CHE
公開徵稿
出版型式 ,電子版