Brazilian Journal of Nephrology (Aug 2024)

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

  • 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

DOI
https://doi.org/10.1590/2175-8239-jbn-2023-0135en
Journal volume & issue
Vol. 46, no. 4

Abstract

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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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