Emerging Approaches in Machine Learning for Identifying Alzheimer's disease ADNI Using Classification Methodologies
Keywords:
Machine Learning, Alzheimer’s, Voting, kNN, DT, XG boost, CBIR technologies, Correlation-based Feature Selection, Open Access Series of Imaging Research (OASIS), MMSEAbstract
Among elderly individuals, among the most common causes of memory loss is Alzheimer's disease (AD). Also, a sizeable amount of individual’s worldwide experience metabolic issues including diabetes and Alzheimer's disease. Degenerative brain changes are brought on by Alzheimer's disease. This sickness can make more people inactive as the older population increases since it affects their cognitive and physical capabilities. Their relatives as well as the financial, industrial, and social sectors may be affected by this. To identify these illnesses sooner, researchers have recently looked into several deep learning, machine learning, and other methodologies. AD individuals can recuperate from this completely while experiencing the least amount of harm by receiving early treatment and timely detection. The model's quality is determined by means of reliability, precise, recall, and F1-measure using the freely accessible series of maging modalities (OASIS) dataset. Our results demonstrated that with the AD dataset, the voting classifier had the highest prediction performance of 95%. As a result, with precise identification, Forecasting models have the ability to significantly cut the yearly incidence and mortality from Alzheimer's disease.
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