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Kulkarni C, Quraishi A, Raparthi M, Shabaz M, Khan MA, Varma RA, Keshta I, Soni M, Byeon H. Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer. BMC Med Inform Decis Mak 2024; 24:92. [PMID: 38575951 PMCID: PMC10996111 DOI: 10.1186/s12911-024-02495-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 03/29/2024] [Indexed: 04/06/2024] Open
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.
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Affiliation(s)
- Chaitanya Kulkarni
- Department of Computer Engineering, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, Pune, 413133, Maharashtra, India
| | - Aadam Quraishi
- M.D. Research, Intervention Treatment Institute, Houston, TX, USA
| | - Mohan Raparthi
- Software Engineer, Alphabet Life Science, Dallas, TX, 75063, USA
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology, Jammu, J&K, India.
| | - Muhammad Attique Khan
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
| | - Raj A Varma
- Symbiosis Law School (SLS), Symbiosis International (Deemed University) (SIU), Vimannagar, Pune, Maharashtra, India
| | - Ismail Keshta
- Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Mukesh Soni
- Dr D Y Patil Vidyapeeth, Dr. D. Y. Patil School of Science and Technology, Pune, 411033, India
| | - Haewon Byeon
- Department of Digital Anti-Aging Healthcare, Inje University, Gimhae, Republic of Korea, 50834
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