Abstract
Alzheimer’s Disease (AD) is the most common form of dementia that can lead to a neurological brain disorder that causes progressive memory loss as a result of damaging the brain cells and the ability to perform daily activities. This disease is one of kind and fatal. Early detection of AD because of its progressive threat and patients all around the world. The early detection is promising as it can help to predetermine the condition of lot of patients they might face in the future. So, by examining the consequences of the disease, using MRI images we can get the help of Artificial intelligence (AI) technology to classify the AD patients if they have or may not have the deadly disease in future. In recent years, AI-based Machine Learning (ML) techniques are very useful for the diagnosis of AD. In this paper, we have applied different machine learning techniques such as Logistic Regression, Decision Tree, Random forest classifier, Support Vector Machine and AdaBoost for the earlier diagnosis and classification of Alzheimer’s disease using Open Access Series of Imaging Studies (OASIS) dataset, in which a significant performance and result gained on classification with Random Forest classifier.
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Baglat, P., Salehi, A.W., Gupta, A., Gupta, G. (2020). Multiple Machine Learning Models for Detection of Alzheimer’s Disease Using OASIS Dataset. In: Sharma, S.K., Dwivedi, Y.K., Metri, B., Rana, N.P. (eds) Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. TDIT 2020. IFIP Advances in Information and Communication Technology, vol 617. Springer, Cham. https://doi.org/10.1007/978-3-030-64849-7_54
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DOI: https://doi.org/10.1007/978-3-030-64849-7_54
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