Alzheimer’s Disease Prediction and Classification Using Machine Learning Models

Alzheimer's Disease Prediction and Classification Using Machine Learning Models

Vidhi Narodiya, Aryan Patel, Dhruv Gadhiya, Asst. Prof. Ms. Monali Parikh
123Research Scholar, Institute of Information Technology, Krishna School Of Emerging Technology & Applied Research,
KPGU University ,Varnama, Vadodara, Gujarat, India

4Assistant Professor, Department of Information Technology and Engineering, Krishna School Of Emerging Technology & Applied Research, KPGU University. Varnama, Vadodara, Gujarat, India

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Abstract — This study addresses the critical challenge of early Alzheimer's disease (AD) detection through machine learning algorithms, leveraging a dataset of 6400 preprocessed MRI images. We explore several models, including Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and Convolutional Neural Networks (CNNs) integrated with VGG16 and EfficientNetB0 architectures. Among these, both CNN models demonstrate exceptional performance, achieving an accuracy of 96% in AD classification. The results underscore the efficacy of deep learning models, particularly CNNs, in accurately distinguishing between various stages of Alzheimer's disease. This study highlights the potential of these models to significantly enhance early AD detection, offering a reliable tool for clinical applications.

Key Words: Alzheimer's Disease (AD) Detection, Early Diagnosis, Machine Learning, Convolutional Neural Networks (CNN), VGG16, EfficientNetB0, MRI Imaging, Deep Learning, Image Classification, Alzheimer's Disease Classification,Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Medical Image Analysis, Neuroimaging, Alzheimer's Detection Accuracy