%0 Journal Article
%T 数据挖掘技术在代谢相关脂肪性肝病预测中应用的研究进展<br>Research Progress in the Application of Data Mining Techniques for Metabolic-Associated Fatty Liver Disease Risk Prediction
%A 邹慧慧
%A 潘梦圆
%A 陈正英
%J Nursing Science
%P 956-965
%@ 2168-5614
%D 2025
%I Hans Publishing
%R 10.12677/ns.2025.146127
%X 代谢相关脂肪性肝病(MAFLD)作为全球最常见的慢性肝病&#65292;其早期预测和精准干预对预防和延缓疾病进展至关重要。近年来&#65292;随着医疗大数据和人工智能技术的快速发展&#65292;数据挖掘技术在代谢相关脂肪性肝病风险预测方面展现出巨大潜力。数据挖掘技术为代谢相关脂肪性肝病的个体化预测和精准干预提供了新工具&#65292;但其临床应用仍需跨学科协作与标准化数据生态支持。本文从数据挖掘及代谢相关脂肪性肝病的概念入手&#65292;对数据挖掘技术在代谢相关脂肪性肝病预测特征选择和模型构建中的应用现状加以总结和讨论&#65292;梳理现有预测模型的局限&#65292;以期为更好发挥数据挖掘技术在代谢相关脂肪性肝病预测的实际效用提供参考。<br>Metabolic associated fatty liver disease (MAFLD), as the most prevalent chronic liver disease globally, requires early prediction and precise intervention to effectively prevent and delay disease progression. In recent years, driven by advancements in medical big data and artificial intelligence technologies, data mining has demonstrated significant potential for risk prediction of MAFLD. Data mining offers novel tools for individualized prediction and precision intervention in MAFLD; however, its clinical application necessitates interdisciplinary collaboration and the establishment of a standardized data ecosystem. This article begins with an overview of the concepts of data mining and MAFLD, systematically summarizing and analyzing the current status of data mining technology in feature selection and model construction for MAFLD prediction. Furthermore, it critically evaluates the limitations of existing prediction models, aiming to provide insights and guidance for maximizing the practical utility of data mining in MAFLD prediction.
%K 代谢相关脂肪性肝病(MAFLD)&#65292
%K 数据挖掘&#65292
%K 预测模型<br>Metabolic-Associated Fatty Liver Disease (MAFLD)
%K Data Mining
%K Prediction Model
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=117214