期刊論文
| 學年 | 112 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2024-04-17 |
| 作品名稱 | Analyzing Longitudinal Health Screening Data with Feature Ensemble and Machine Learning Techniques: Investigating Diagnostic Risk Factors of Metabolic Syndrome for Chronic Kidney Disease Stages 3a to 3b |
| 作品名稱（其他語言） | |
| 著者 | Yang, Chih-te |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Diagnostics 14(8), 825 |
| 摘要 | Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models—Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost—each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them. |
| 關鍵字 | chronic kidney disease;metabolic syndrome;feature ensemble;machine learning;longitudinal data;health screening |
| 語言 | en |
| ISSN | 2075-4418 |
| 期刊性質 | 國外 |
| 收錄於 | SCI Scopus |
| 產學合作 | |
| 通訊作者 | Chi-Jie Lu |
| 審稿制度 | 是 |
| 國別 | CHE |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/125703 ) |