Prediction of metabolic syndrome and its associated risk factors in patients with chronic kidney disease using machine learning techniques.

IF 1.3 Q3 UROLOGY & NEPHROLOGY Jornal brasileiro de nefrologia : 'orgao oficial de Sociedades Brasileira e Latino-Americana de Nefrologia Pub Date : 2024-10-01 DOI:10.1590/2175-8239-JBN-2023-0135en

Jalila Andréa Sampaio Bittencourt, Carlos Magno Sousa Junior, Ewaldo Eder Carvalho Santana, Yuri Armin Crispim de Moraes, Erika Cristina Ribeiro de Lima Carneiro, Ariadna Jansen Campos Fontes, Lucas Almeida das Chagas, Naruna Aritana Costa Melo, Cindy Lima Pereira, Margareth Costa Penha, Nilviane Pires, Edward Araujo Júnior, Allan Kardec Duailibe Barros Filho, Maria do Desterro Soares Brandão Nascimento

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Abstract

Introduction: Chronic kidney disease (CKD) and metabolic syndrome (MS) are recognized as public health problems which are related to overweight and cardiometabolic factors. The aim of this study was to develop a model to predict MS in people with CKD.

Methods: This was a prospective cross-sectional study of patients from a reference center in São Luís, MA, Brazil. The sample included adult volunteers classified according to the presence of mild or severe CKD. For MS tracking, the k-nearest neighbors (KNN) classifier algorithm was used with the following inputs: gender, smoking, neck circumference, and waist-to-hip ratio. Results were considered significant at p < 0.05.

Results: A total of 196 adult patients were evaluated with a mean age of 44.73 years, 71.9% female, 69.4% overweight, and 12.24% with CKD. Of the latter, 45.8% had MS, the majority had up to 3 altered metabolic components, and the group with CKD showed statistical significance in: waist circumference, systolic blood pressure, diastolic blood pressure, and fasting blood glucose. The KNN algorithm proved to be a good predictor for MS screening with 79% accuracy and sensitivity and 80% specificity (area under the ROC curve - AUC = 0.79).

Conclusion: The KNN algorithm can be used as a low-cost screening method to evaluate the presence of MS in people with CKD.

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利用机器学习技术预测慢性肾病患者的代谢综合征及其相关风险因素。

导言：慢性肾脏病（CKD）和代谢综合征（MS）是公认的公共健康问题，与超重和心脏代谢因素有关。本研究的目的是建立一个模型来预测慢性肾脏病患者的代谢综合征：这是一项前瞻性横断面研究，研究对象是巴西马萨诸塞州圣路易斯市一家参考中心的患者。样本包括根据轻度或重度慢性肾脏病分类的成年志愿者。在 MS 跟踪中，使用了 k-nearest neighbors (KNN) 分类器算法，输入参数包括：性别、吸烟、颈围和腰臀比。结果以 p < 0.05 为有意义：共评估了 196 名成年患者，平均年龄为 44.73 岁，71.9% 为女性，69.4% 超重，12.24% 患有慢性肾脏病。在后者中，45.8%的人患有多发性硬化症，大多数人有多达三种代谢成分的改变，而患有慢性肾脏病的人群在腰围、收缩压、舒张压和空腹血糖方面都有统计学意义。事实证明，KNN 算法是一种很好的多发性硬化症筛查预测方法，准确率和灵敏度为 79%，特异性为 80%（ROC 曲线下面积 - AUC = 0.79）：结论：KNN 算法可作为一种低成本筛查方法，用于评估慢性肾脏病患者是否患有多发性硬化症。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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