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

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.

Phenotypic Characterization of Chronic Kidney Patients Through Hierarchical Clustering.

Chronic kidney disease is a major public health problem around the world and this disease early diagnosis is still a great challenge as it is asymptomatic in its early stages. Thus, in order to identify variables capable of assisting CKD diagnosis and monitoring, machine learning techniques and statistical analysis use has shown itself to be extremely promising. For this work, unsupervised machine learning, statistical analysis techniques and discriminant analysis were used.