NGOL: An Enhanced Vascular Dementia Diagnosis System Design based on Learning enabled Neural Gradient Optimization Logic

Vascular dementia is a common form of neurodegenerative disorder that impairs the cognitive ability, but it is difficult to diagnose as it shares symptoms with other dementi as and the lesion can be heterogeneous. Conventional machine learning and deep learning methods are associated with generalization, low interpretability, and weak multimodal combination. In order to overcome these shortcomings, this work suggests NGOL (Neural Gradient Optimization Logic), a improved diagnostics model that combines both MRI-based structural biomarkers and clinical measurements via an adaptive multimodal network. The system has intensity-corrected MRI preprocessing and PCA-enabled feature compression as well as gradient-optimized neural learning using second-order updates in order to achieve better parameter convergence. The experimental analysis was done on a dataset of 3,650 patient records and neuroimaging scans obtained in clinical archives and benchmark repositories. The NGOL model demonstrated a total score of 97.8% accuracy, a recall of 98.1, precision of 97.2 and Fl-score of 97.6, higher than the currently existing models which include CNN (93.4%), Bi-LSTM (94.2%), and Random Forest (91.8%). The framework achieved 98.3 and 97.9% sensitivity in microbleed recognition and lacunar infarct recognition respectively in lesion severity detection. The 10- fold cross-validation showed the strong performance of the model with the consistency range of 97.5% to 98.5% of the AUC-ROC. The adaptive gradient reasoning made the cases of early vascular impairment to be significantly less false. These findings indicate that the NGOL framework is clinically reliable, predictively consistent, and better than traditional diagnostic tools. The system has a high potential of being an intelligent decision-support health care system in neurological-assisted dementia screening.