Boruta Feature Selection and Deep Learning for Alzheimer’s Disease Classification

International Journal of Statistics in Medical Research, Journal Year: 2025, Volume and Issue: 14, P. 145 - 152

Published: March 25, 2025

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and functional deterioration. The early accurate classification of AD crucial for timely intervention management. This study utilizes the Boruta feature selection method to identify most relevant features classification, selecting top 15 based on importance ranking. Three machine learning models—Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), Support Vector Machines (SVM)—were evaluated using accuracy, precision, recall, F1-score as performance metrics. LSTM model demonstrated highest accuracy (89.30%), outperforming DNN (88.14%) SVM (84.19%), owing its capability capturing temporal dependencies in inpatient data. Results indicate that deep models offer superior compared traditional approaches classification. emphasizes cognitive, lifestyle, metabolic diagnosis while acknowledging limitations such dataset constraints interpretability. Future research should improve explainability, incorporate multi-modal data, leverage real-time monitoring techniques enhanced detection.

Language: Английский

From Cognitive Systems to Alzheimer's Disease: The Role of Computational Modeling

The Neuroscience Journal of Shefaye Khatam, Journal Year: 2024, Volume and Issue: 13(1), P. 63 - 73

Published: Dec. 1, 2024

Language: Английский