Disease Detection: Alzheimer’s Detection Using Machine Learning

This research delves into the utilization of machine learning methodologies for Alzheimer’s Disease detection. Various algorithms, encompassing Support Vector Machines (SVM), Random Forests, Neural Networks, and Gradient Boosting, are under scrutiny for their effectiveness in identifying Alzheimer’s disease. Diverse datasets incorporating MRI and PET scan imaging data, genetic markers, and cognitive assessments are employed to evaluate these algorithms. Performance metrics, including accuracy, sensitivity, specificity, and the area under the curve (AUC), are scrutinized to comprehend the challenges of generalizing these models across diverse populations and datasets. Addressing limitations, ethical considerations, and potential future directions, this study aims to enhance the accuracy and early diagnosis of Alzheimer’s disease through machine learning methodologies. The insights aim to contribute to improved healthcare practices, early intervention, and disease management for individuals affected by or at risk of Alzheimer’s.