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Automated classification of Alzheimer's disease based on deep belief neural networks

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Abstract

When it comes to the causes of dementia, Alzheimer's disease is the most mysterious. There is no central genetic component connected to Alzheimer's disease. Previous approaches and tools for determining Alzheimer's disease genetic risk factors are unreliable. The brain images provided the bulk of the available information. In contrast, large-scale approaches in bioinformatics have seen significant development in recent years. It has encouraged efforts to identify the hereditary risk factors for develo** Alzheimer's disease. A large amount of data on the brain's prefrontal cortex as a consequence of recent studies has allowed for the creation of classification and prediction models for Alzheimer's disease. Using the OASIS-4 dataset, which suffers from High Dimension Low Sample Size (HDLSS) problems, a Deep belief network with a Restricted Boltzmann Machine (RBM)-based classification model for processing multimodal data has been constructed. The multi-layer feature selection procedure that took into account both the technical and biological aspects of the characteristics to solve the HDLSS problem has been proposed. In molecular-level information, in the first stage of the two-tiered feature selection method, abnormal places in the dataset are found. Second, combining multiple different feature selection methods is used to refine the set of candidate genes. The principal component analysis is used for dimensionality reduction in MRI, and well pre-processed cognitive assessment scores like MMSE and ADA-cog are considered. Deep belief networks with multiple RBM are used to do unsupervised feature learning. Fivefold cross-validation has been used in all classification studies.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Computer Applications, Kongu Engineering College, Perundurai, Erode, India

    K. Nanthini, A. Tamilarasi & D. Sivabalaselvamani

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

    P. Suresh

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  1. K. Nanthini
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  2. A. Tamilarasi
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Correspondence to K. Nanthini.

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The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. To the best of my knowledge and belief, any actual, perceived, or potential conflicts between my duties as an employee and my private and/or business interests have been fully disclosed in this form in accordance with the requirements of the journal.

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Nanthini, K., Tamilarasi, A., Sivabalaselvamani, D. et al. Automated classification of Alzheimer's disease based on deep belief neural networks. Neural Comput & Applic 36, 7405–7419 (2024). https://doi.org/10.1007/s00521-024-09468-6

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  • DOI: https://doi.org/10.1007/s00521-024-09468-6

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