Neuro-Cognitive Pattern Recognition: Advancements in Memory-Related Disorders Identification

The application of machine learning in medical imaging and diagnostics opens up new pathways for disease identification and classification. Our research investigates the use of various machine learning models for Alzheimer's disease detection and classification using MRI image data, including Support Vector Machines (SVM) with different kernels, Linear Discriminant Analysis (LDA), and the advanced convolutional neural network, EfficientNetB0. The comparative study is an important component of this research, concentrating not only on the models' performance but also on the theoretical assumptions that underpin their efficacy. This investigation sheds light on why certain models outperform others, notably in the context of Alzheimer's disease classification. The research emphasizes the robust performance of linear models such as SVM and LDA, as well as the complexities of convolutional neural networks. Notably, our findings show a tremendous success with a 98.8 % accuracy rate, highlighting the potential of machine learning in improving diagnostic processes. This study contributes to the expanding field of machine learning in medical diagnostics by providing in-depth analysis and theoretical understanding that guide model selection and optimization in high-stakes healthcare applications.