Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach.

ABSTRACT:

Background

Identification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM.

Objectives

We aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators.

Methods

In this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two

SUBMITTER: Lv X 

PROVIDER: S-EPMC10982324 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature