Machine learning models for early-stage Alzheimer's prediction
2022
Sathyabama Institute of Science and Technology, Chennai, India
In its early stages, Alzheimer's disease (AD) is hard to predict. A treatment given at an early stage of AD is more effective, and it causes fewer minor damage than a treatment done at a later stage. In this study, several computational techniques have been employed to identify the best parameters for Alzheimer's disease prediction. Predictions of Alzheimer's disease are based on MRI images from 150 patients from the Open Access Series of Imaging Studies (OASIS) database. Machine learning techniques were applied to Alzheimer's disease datasets to bring a new dimension to predict the disease at an early stage. The proposed classification scheme can be used by clinicians to make diagnoses of these diseases. The proposed work shows better results, with the best validation average accuracy of 83% on the test data of AD. This test accuracy score is significantly higher in comparison with existing works.
Early-stage Alzheimer's disease prediction using machine learning models
C. Kavitha
Added on: 09-14-2023
[1] https://www.frontiersin.org/articles/10.3389/fpubh.2022.853294/full[2] https://www.drugtargetreview.com/article/110227/the-future-of-central-nervous-system-research/?utm_source=Email+marketing&utm_medium=email&utm_campaign=DTR+-+Industry+Insight+-+Quantum+-+01.09.2023&utm_term=Computational+tool+gets+more+out+of+multi-omics+data&utm_content=https%3a%2f%2femails.drugtargetreview.com%2frussellpublishinglz%2f&gator_td=mIEcIc9i81kCKVEhzhGoua2AdBgsh3MXlma4DaxbM%2bxK7rKyR%2fQcxk8ybkHB1l%2bRXEvytgBCnnivdmqswbwYTxn4iGykbFmGXXK7dz8FnJzV82Okhr4PBfh1smePTncFC5cYRgtu%2f2Az8umlaHaYwOrkQf3awJMA0VuCviYYObU2xCPqj4rl%2b6sSM5T5exVI%2fzk%2b0uf1Z1EzIpnMw8X8kEumOE8bMB8%2f1hAmVz6Yx%2fw%3d
