Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening.

ABSTRACT: Most patients with diabetes mellitus are asymptomatic, which leads to delayed and more complex treatment. At the same time, most individuals are routinely subjected to standard clinical laboratory examinations, which create large health datasets over a lifetime. Computer processing has been used to search for health anomalies and predict diseases using clinical examinations. This work studied machine learning models to support the screening of diabetes through routine laboratory tests using data from laboratory tests of 62,496 patients. The classification and regression models used were the K-nearest neighbor, support vector machines, Bayes naïve, random forest models, and artificial neural networks. Glycated hemoglobin, a test used for diabetes diagnosis, was used as the target. Regressio

SUBMITTER: Cardozo G 

PROVIDER: S-EPMC8983182 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature