Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis.

ABSTRACT: Type 2 Diabetes Mellitus (T2DM) is a significant public health problem globally. The diagnosis and management of diabetes are critical to reduce the diabetes complications including cardiovascular disease and cancer. This study was designed to assess the potential association between T2DM and routinely measured hematological parameters. This study was a subsample of 9000 adults aged 35-65 years recruited as part of Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study. Machine learning techniques including logistic regression (LR), decision tree (DT) and bootstrap forest (BF) algorithms were applied to analyze data. All data analyses were performed using SPSS version 22 and SAS JMP Pro version 13 at a significant level of 0.05. Based on the performance indices, the BF mod

SUBMITTER: Mansoori A 

PROVIDER: S-EPMC9837189 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature