Advancing Alzheimer’s Prediction With Hvbo: A Hybrid Temporal and Spatial Vision Boost Model

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that poses considerable challenges to healthcare systems worldwide. Early detection is critical for effective intervention; however current diagnostic procedures frequently lack the required precision and sensitivity. This research proposes a novel Hybrid Spatial Vision Boost classifier (HSVBC) model to enhance the accuracy and reliability of AD detection.

The approach preprocesses MRI images using an Isotropic Adaptive Preserving Filter (IAPF), efficiently reducing noise while maintaining critical edges and structural information. The Dual Intensity Spatial Matrix (DISM) is proposed to extracts features by capturing complicated spatial patterns and intensity values, resulting in a comprehensive data representation.

To solve the problem of high dimensionality, Hybrid Discriminant Analysis (HDA) is proposed to reduce dimensionality while preserving discriminative information, resulting in improved precision and reduced computational complexity. The XGBoost classifier is used for the final classification, improving AD prediction accuracy and efficiency.

The proposed approach is evaluated using the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset, which obtained measures such as accuracy 99.85%, precision 99.87%, recall 99.43%, and F1 scores 99.65%. These results demonstratesignificant improvements over traditional techniques, offering a promising early and accurate AD detection solution.