Alzheimer Disease Prediction Through Guided Predictive Modeling With Machine Learning
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Abstract
Alzheimer's disease is a progressive neurodegenerative disorder characterized by the accumulation of misfolded brain proteins, especially beta-amyloid plaques, resulting in cognitive deterioration and memory impairment. However, there has been no effort of early detection to facilitate prompt intervention and preventive strategies. This research fulfills this essential need by employing the Open Access Series of Imaging Studies (OASIS) dataset supplied by the Alzheimer's Disease Neuroimaging Initiative (ADNI). The research employs the Cross-industry Standard Process for Data Mining (CRISP-DM) methodology to create and assess a classification model utilizing Artificial Neural Networks (ANN). The model attains a remarkable accuracy rate of 96%, exhibiting elevated precision, recall, and F1-scores across all categories. A 10-fold cross-validation technique was utilized to assess the model's robustness, resulting in an average accuracy of 90.7%. These findings underscore the efficacy of artificial neural networks in identifying Alzheimer's disease in its initial phases. This research utilizes advanced data mining approaches to improve predictive capacities and highlights the promise of machine learning in tackling intricate healthcare issues.
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