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基于预测糖尿病肾病数学模型的最新进展对传统数学模型与机器学习模型的比较

Comparison of conventional mathematical model and machine learning model based on recent advances in mathematical models for predicting diabetic kidney disease.

作者信息

Sheng Yingda, Zhang Caimei, Huang Jing, Wang Dan, Xiao Qian, Zhang Haocheng, Ha Xiaoqin

机构信息

Gansu University of Chinese Medicine, Lanzhou, Gansu, China.

The 940th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army, Lanzhou, Gansu, China.

出版信息

Digit Health. 2024 Mar 6;10:20552076241238093. doi: 10.1177/20552076241238093. eCollection 2024 Jan-Dec.

Abstract

Previous research suggests that mathematical models could serve as valuable tools for diagnosing or predicting diseases like diabetic kidney disease, which often necessitate invasive examinations for conclusive diagnosis. In the big-data era, there are several mathematical modeling methods, but generally, two types are recognized: conventional mathematical model and machine learning model. Each modeling method has its advantages and disadvantages, but a thorough comparison of the two models is lacking. In this article, we describe and briefly compare the conventional mathematical model and machine learning model, and provide research prospects in this field.

摘要

先前的研究表明,数学模型可以作为诊断或预测糖尿病肾病等疾病的宝贵工具,而这些疾病通常需要进行侵入性检查才能做出确定性诊断。在大数据时代,有几种数学建模方法,但一般来说,公认的有两种类型:传统数学模型和机器学习模型。每种建模方法都有其优缺点,但缺乏对这两种模型的全面比较。在本文中,我们描述并简要比较了传统数学模型和机器学习模型,并提供了该领域的研究前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8d8/10921860/5d2376a8f334/10.1177_20552076241238093-fig1.jpg

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