Alzheimer’s Disease: Classification and Detection using MRI Dataset
Suhaira V P1, Sita S2, Joby George3
1Suhaira V P, Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India.
2Sita S, Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India.
3Prof. Joby George, Associate Professor and Head, Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India
Manuscript received on March 12, 2021. | Revised Manuscript received on March 16, 2021. | Manuscript published on March 30, 2021. | PP: 70-72 | Volume-10 Issue-5, March 2021 | Retrieval Number: 100.1/ijitee.E86620310521| DOI: 10.35940/ijitee.E8662.0310521
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Alzheimer’s disease (AD) is a hereditary brain condition that is incurable and progresses over time. Patients with Alzheimer’s disease experience memory loss, uncertainty, and difficulty speaking, reading, and writing as a result of this condition. Alzheimer’s disease eventually affects the portion of the brain that controls breathing and heart function, leading to death. This framework proposes the OASIS (Open Access Series of Imaging Studies) dataset, which contains the existing MRI data set, which is comprised of a longitudinal sample of 150 subjects aged 60 to 96 who were all acquired on the same scanner using similar sequences. This paper uses a combination of brain MRI scans and psychological parameters to predict disease with high accuracy using various classifier algorithms, and the results can be compared to improve performance.
Keywords: Alzheimer’s Disease, Support Vector Machine, Dementia, OASIS.