Latent Feature‐Based Type 2 Diabetes Prediction Using a Hybrid Stacked Sparse Autoencoder and Machine Learning Models

ABSTRACT Early and precise prediction of Type 2 diabetes is vital for effective intervention. However, extracting meaningful insights from high‐dimensional datasets with sparse values remains challenging. Sparsity and redundant features often hinder traditional machine learning algorithms' abil...

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Bibliographic Details

Published in:	Engineering reports (Hoboken, N.J.) Vol. 7; no. 9
Main Authors:	Abdussamad, Daud, Hanita, Sokkalingam, Rajalingam, Zubair, Muhammad, Khan, Iliyas Karim, Mahmood, Zafar
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 01.09.2025 Wiley
Subjects:	autoencoder hybrid model machine learning models sparse data Type 2 diabetes prediction
ISSN:	2577-8196, 2577-8196
Online Access:	Get full text
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