Machine learning based models for prediction of subtype diagnosis of primary aldosteronism using blood test.

ABSTRACT: Primary aldosteronism (PA) is associated with an increased risk of cardiometabolic diseases, especially in unilateral subtype. Despite its high prevalence, the case detection rate of PA is limited, partly because of no clinical models available in general practice to identify patients highly suspicious of unilateral subtype of PA, who should be referred to specialized centers. The aim of this retrospective cross-sectional study was to develop a predictive model for subtype diagnosis of PA based on machine learning methods using clinical data available in general practice. Overall, 91 patients with unilateral and 138 patients with bilateral PA were randomly assigned to the training and test cohorts. Four supervised machine learning classifiers; logistic regression, support vector machines,

SUBMITTER: Kaneko H 

PROVIDER: S-EPMC8096956 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature