A hybrid multimodal machine learning model for Detecting Alzheimer's disease

• Published in: Computers in biology and medicine Vol. 170; p. 108035
• Main Authors: Sheng, Jinhua, Zhang, Qian, Zhang, Qiao, Wang, Luyun, Yang, Ze, Xin, Yu, Wang, Binbing
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2024; Elsevier Limited
• Subjects: Accuracy; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Anatomy; Artificial intelligence; Artificial neural networks; Biomarkers; Brain mapping; Brain research; Cerebrospinal fluid; Classification; Cognitive ability; Cognitive Dysfunction - diagnosis; Comparative analysis; Deep learning; Dementia; Diagnosis; Feature selection; Harris hawks optimization; Heuristic methods; Humans; Internal Medicine; Iterative methods; Kernel extreme learning machine; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Multimodality; Neural networks; Neurodegenerative diseases; Neuroimaging; Neuroimaging - methods; Operators (mathematics); Optimization; Optimization techniques; Other; Positron emission; Positron emission tomography; Positron-Emission Tomography - methods; Support vector machines; Multimodality; Alzheimer's disease; Machine learning; Kernel extreme learning machine; Harris hawks optimization

Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has limitations. Multimodal fusion of complementary biomarkers may improve diagnostic performance. This study proposes a multimodal machine learning framework integrating magnetic resonance imaging (MRI), positron em...