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使用机器学习技术预测慢性病患者的代谢综合征及其相关风险因素

Jalila Andréa Sampaio Bittencourt¹, Carlos Magno Sousa Junior², Ewaldo Eder Carvalho Santana²

¹Universidade Federal do Maranhão, Departamento de Engenharia Eletrônica, Laboratório de Processamento da Informação Biológica, São Luiz, MA, Brazil.

Jornal brasileiro de nefrologia

|August 12, 2024

概括

一个k-最近邻居 (KNN) 算法有效地选慢性病 (CKD) 患者的代谢综合征 (MS). 这种低成本的方法显示出良好的准确性,有助于早期发现和管理这些相互关联的健康问题.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
心脏病学心脏病学
数据科学数据科学数据科学

背景情况:

慢性病 (CKD) 和代谢综合征 (MS) 是严重的公共卫生问题.
这两种情况经常与超重和心脏代谢风险因素有关.
在CKD患者中预测MS对于管理相关健康风险至关重要.

研究的目的:

开发和验证慢性病患者代谢综合征 (MS) 的预测模型.
评估k-最接近邻居 (KNN) 算法在该人群中进行MS查的有效性.

主要方法:

一项前性横截面研究与巴西的成年患者进行.
使用k-最近邻居 (KNN) 分类器来检测MS.
输入变量包括性别,吸烟状态,子周长和腰与部的比例.

主要成果:

这项研究评估了196名成年患者,其中12.24%患有CKD.
在CKD患者中,45.8%被诊断为MS.
在MS查中,KNN算法获得了79%的准确性和80%的特异性 (AUC = 0.79).

结论:

k-最近邻居 (KNN) 算法证明了其作为一种具有成本效益的诊断工具的实用性,用于对慢性病患者的MS进行查.
这种方法可以促进MS的早期识别,从而有可能改善患者的治疗结果.

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