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International Journal of Biotech Trends and Technology (IJBTT) |  |
| © 2023 by IJBTT Journal | ||
| Volume - 13 Issue - 3 | ||
| Year of Publication : 2023 | ||
| Authors : Shiva Kumar Sriramulugari, Venkata Ashok K Gorantla | ||
| DOI : 10.14445/22490183/IJBTT-V13I3P601 |
How to Cite?
"Deep Learning Based Convolutional Geometric Group Network for Alzheimer Disease Prediction" International Journal of Biotech Trends and Technology vol. 13, no. 3, pp. 1-6, 2023. Crossref, https://doi.org/10.14445/22490183/IJBTT-V13I3P601
AbstractAlzheimer's Disease (AD) is the most prevalent form of dementia among the elderly. A rising interest in applying machine learning to discover the origins of prevalent metabolic illnesses like Alzheimer's and Diabetes has emerged. The alarming annual rise in their frequency is really worrying. In Alzheimer's disease, brain cells deteriorate, leading to the illness's hallmark symptoms. As the global population ages, so too will the incidence of diseases that cause cognitive and physical decline. This will have far-reaching monetary, social, and economic consequences. Diagnosing Alzheimer's disease in its early stages is challenging. Treatments for Alzheimer's disease have a higher success rate and fewer side effects if given early on. As a result, in this study, an original Adam-optimized Convolutional geometric group network was built to detect dementia in its earliest stages. Accuracy is used to evaluate the success of Open Access Kaggle data used to make Alzheimer's disease predictions. Clinicians will be able to utilize the suggested categorization approach to identify these conditions in their patients correctly. The proposed approach has the potential to significantly improve yearly death rates associated with Alzheimer's disease by facilitating earlier detection. The suggested study improves upon prior efforts, as shown by a 95.7% average accuracy in validating AD test data. The test's accuracy is far greater than that of previous efforts.
Keywords
Alzheimer's Disease, prediction, Adam optimization, Convolutional geometric group network.
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