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

doi:10.3390/diagnostics14080825

淡江大學機構典藏 > 商管學院 > 企業管理學系暨研究所 > 期刊論文 > Item 987654321/125703

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/125703

Title:	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
Authors:	Ming-Shu Chen;Tzu-Chi Liu;Mao-Jhen Jhou;Chih-Te Yang;Chi-Jie Lu
Keywords:	chronic kidney disease;metabolic syndrome;feature ensemble;machine learning;longitudinal data;health screening
Date:	2024-04-17
Issue Date:	2024-07-31 12:09:19 (UTC+8)
Publisher:	MDPI
Abstract:	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.
Relation:	Diagnostics 14(8), 825
DOI:	10.3390/diagnostics14080825
Appears in Collections:	[Graduate Institute & Department of Business Administration] Journal Article

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