Advancements in Alzheimer's Disease Classification: Integrating Machine Learning, Neuroimaging, and Biomarkers

Alzheimer's disease (AD), a progressive neurodegenerative disorder, leads to cognitive decline, memory loss, and impaired daily functioning. Early detection and precise classification are critical for timely intervention and personalized care. These abstract reviews recent advancements in brain disease classification, particularly for AD, highlighting the use of machine learning algorithms, neuroimaging methods, and biomarker analysis. Machine learning models trained on neuroimaging data, such as MRI and PET scans, have demonstrated efficacy in distinguishing Alzheimer's disease, mild cognitive impairment (MCI), and healthy individuals. Biomarker studies involving cerebrospinal fluid (CSF) and blood samples provide critical insights into AD pathology, supporting disease classification efforts. Integrating diverse data types, including imaging, genetic, and clinical information, can significantly enhance the accuracy and reliability of classification models. Emerging deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), enable the extraction of complex patterns from heterogeneous data sources, improving classification outcomes. Nonetheless, challenges persist, such as the requirement for large-scale, multi-centre datasets, uniform imaging protocols, and greater interpretability of machine learning models. Keywords: Alzheimer's disease; Machine Learning; Neuroimaging; Biomarkers