Alzheimer's Disease Prediction using an Artificial Butterfly Optimizer with a Two-Layer CNN Approach

Alzheimer's disease, the most common dementia, involves brain damage. Alzheimer's starts with mild symptoms and worsens over time, making it difficult to treat. Early detection can prevent disease progression or reduce symptoms before major impairment. This study uses multiple stages to detect Alzheimer's early. Model training, feature selection, and thorough data preprocessing are system essentials. Data scaling, missing value imputation, and outlier treatment prepared the clinical dataset. Information gain, correlation coefficient, and chi-square were used to choose Alzheimer's disease predictors. The model was trained on hundreds of clinical samples. The ABO-2layer CNN architecture achieved a maximum prediction accuracy of 94.56%, indicating that preprocessing and feature selection improve model performance. The high anticipated accuracy showed the significance of Alzheimer's disease early detection methods. This study improves feature selection and uses advanced CNN architectures to create tools that may help diagnose Alzheimer's early, one way to battle neurodegenerative diseases.