A Framework for Integrating Multimodal Data for Comprehensive Alzheimer's Disease Diagnosis

Alzheimer's Disease (AD) diagnosis requires a multifaceted approach, integrating neuroimaging, cognitive assessments, and genetic data for comprehensive evaluation. However, existing diagnostic methods often lack accuracy and fail to effectively integrate these diverse data types, leading to inconsistencies and delayed detection. To address these challenges, The proposed Unified Predictive Model using Machine Learning (UPM-ML), a novel framework for neuroimaging analysis, natural language processing for cognitive assessments, and genomic feature extraction for genetic profiling. This integration enhances predictive accuracy and enables early diagnosis. The UPM-ML framework leverages ensemble learning techniques and multimodal fusion to ensure seamless data integration and improved classification performance. By utilizing advanced feature selection and model optimization, the proposed method provides a more reliable and interpretable diagnostic outcome. Experimental results demonstrate that UPM-ML significantly outperforms traditional diagnostic models in accuracy, sensitivity, and specificity. The findings indicate that integrating diverse biomarkers through machine learning can enhance early detection and personalized treatment planning for Alzheimer's disease, paving the way for more effective interventions.