An investigation of machine learning algorithms and data augmentation techniques for diabetes diagnosis using class imbalanced BRFSS dataset

An investigation of machine learning algorithms and data augmentation techniques for diabetes diagnosis using class imbalanced BRFSS dataset

Diabetes is a prevalent chronic condition that poses significant challenges to early diagnosis and identifying at-risk individuals. Machine learning plays a crucial role in diabetes detection by leveraging its ability to process large volumes of data and identify complex patterns. However, imbalance...

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Bibliographic Details
Main Authors: Mohammad Mihrab Chowdhury, Ragib Shahariar Ayon, Md Sakhawat Hossain
Format: Article
Language:English
Published: Elsevier 2024-06-01
Series:Healthcare Analytics
Subjects:
Diabetes
Imbalanced data
Machine learning
Classification
Nominal data
Sampling
Online Access:http://www.sciencedirect.com/science/article/pii/S2772442523001648

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