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Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer.

Authors :
Kulkarni, Chaitanya
Quraishi, Aadam
Raparthi, Mohan
Shabaz, Mohammad
Khan, Muhammad Attique
Varma, Raj A.
Keshta, Ismail
Soni, Mukesh
Byeon, Haewon
Source :
BMC Medical Informatics & Decision Making; 4/5/2024, Vol. 24 Issue 1, p1-14, 14p
Publication Year :
2024

Abstract

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14726947
Volume :
24
Issue :
1
Database :
Complementary Index
Journal :
BMC Medical Informatics & Decision Making
Publication Type :
Academic Journal
Accession number :
176609587
Full Text :
https://doi.org/10.1186/s12911-024-02495-2