Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the gradual deterioration of memory and cognitive functions. While no curative treatment currently exists, early detection can potentially mitigate disease progression. Given the projected twofold increase in prevalence in the near future, timely diagnosis is critical for both affected individuals and healthcare systems. Magnetic resonance imaging plays a pivotal role in the examination of brain structure and the detection of neuroanatomical abnormalities. Demographic information provides contextual data about the study population, while the Mini-Mental State Examination serves as a key instrument for the assessment of cognitive function and the monitoring of longitudinal changes. This study combines neuroimaging, cognitive, and biomarker data to enhance model training and boost predictive accuracy. Machine learning (ML) techniques are employed to analyze large datasets and identify patterns related to the early stages of AD. Common approaches in these analyses include k-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machines (SVM). SVM is particularly effective in high-dimensional spaces, RF utilizes an ensemble of decision trees, and KNN provides a more straightforward method. The research seeks to refine the classification of AD into four stages: mild, moderate, very mild, and normal. By harnessing the capabilities of these ML models, the study aims to improve patient outcomes through earlier interventions, thereby substantially enhancing the quality of life for individuals affected by AD.
Jabasheela et al. (Fri,) studied this question.
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