Leveraging Advanced Artificial Intelligence Paradigms for Proactive Prediction and Diagnostic Analysis of Alzheimer's Disease Pathogenesis

Alzheimer's disease is a procedure of dementia that manifests as the following illness in human brains. It is important to diagnose this type of illness early so that appropriate care can be implemented. By incorporating sophisticated artificial imaging standards as the viable approach for diagnosing disease together with early treatment methods, great progress can be made in improving innovative diagnoses. We propose a new proactive technique aimed at the detection and diagnosis of Alzheimer's disease in this piece of scientific research. The approach uses a Wiener filter for pre-processing to enhance the sharpness of edges and the quality of the images and the Otsu method for segmentation as part of region isolation. The CNN is incorporated into the Election Optimization Algorithm (EOA) to optimize weights to enhance classification outcomes tremendously. Compared with the traditional algorithms like CNN-WO, CNN-GWO, and CNN-PSO, it is proven that the proposed here model is better. Through the experimental results, we underline the fact that the highest value of Accuracy in percentage is near 97 % which confirms the rate of definitive diagnosis. Accuracy is over 95% and errors below 5% by Precision, Recall, F1-Score, and Specificity values are higher than 0.9 and prove the high reliability of the employed model in distinguishing the true positives from true negatives. However, conventional techniques have lower accuracy and the worst performing algorithm is CNN-PSO.