Prediction of diabetes disease using an ensemble of machine learning multi-classifier models.

ABSTRACT:

Background and objective

Diabetes is a life-threatening chronic disease with a growing global prevalence, necessitating early diagnosis and treatment to prevent severe complications. Machine learning has emerged as a promising approach for diabetes diagnosis, but challenges such as limited labeled data, frequent missing values, and dataset imbalance hinder the development of accurate prediction models. Therefore, a novel framework is required to address these challenges and improve performance.

Methods

In this study, we propose an innovative pipeline-based multi-classification framework to predict diabetes in three classes: diabetic, non-diabetic, and prediabetes, using the imbalanced Iraqi Patient Dataset of Diabetes. Our framework incorporates various pre-processing techni

SUBMITTER: Abnoosian K 

PROVIDER: S-EPMC10496262 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature