Exploration of Machine Learning for Hyperuricemia Prediction Models Based on Basic Health Checkup Tests.

ABSTRACT:

Background

Machine learning (ML) is a promising methodology for classification and prediction applications in healthcare. However, this method has not been practically established for clinical data. Hyperuricemia is a biomarker of various chronic diseases. We aimed to predict uric acid status from basic healthcare checkup test results using several ML algorithms and to evaluate the performance.

Methods

We designed a prediction model for hyperuricemia using a comprehensive health checkup database designed by the classification of ML algorithms, such as discrimination analysis, K-nearest neighbor, naïve Bayes (NBC), support vector machine, decision tree, and random forest classification (RFC). The performance of each algorithm was evaluated and compared with the performance of

SUBMITTER: Lee S 

PROVIDER: S-EPMC6406925 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature