Alzheimer's Disease Detection using GPT-4 with LBP Feature Extraction, and Stochastic Simulated Quantum Annealing Optimization and Monitoring and Personalized Recommendation Provisioning in 5g Edge Enabled Cognitive IOT

Abstract

Alzheimer's disease (AD) presents a significant global health challenge, emphasizing the need for advanced diagnostic methodologies. This study proposes an innovative approach for AD detection by integrating natural language processing and quantum-inspired optimization techniques. The research employs the powerful language model GPT-4 for text analysis, extracting valuable information from clinical reports and narratives related to AD patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Additionally, Local Binary Pattern (LBP) feature extraction is applied to enhance the representation of neuroimaging data. The fusion of GPT-4-processed textual information and LBP-enhanced image features aims to provide a comprehensive understanding of AD manifestations. To optimize the diagnostic accuracy, Stochastic Simulated Quantum Annealing (SSQA) is introduced as a novel computational approach. SSQA leverages quantum-inspired strategies to efficiently navigate the complex space of diagnostic patterns. Additionally, the study extends its focus to the area of 5G edge-enabled Cognitive Internet of Things (IoT) for monitoring and personalized recommendation provisioning. Leveraging the capabilities of 5G edge computing, the cognitive IoT system facilitates real-time monitoring of diverse health parameters related to AD. This includes the seamless integration of sensor data, medical records, and personalized information through edge computing nodes. The proposed system not only monitors health metrics but also employs cognitive intelligence to provide personalized recommendations, optimizing patient care and well-being. The experimental evaluation on the ADNI dataset demonstrates the efficacy of the proposed methodology in detecting early signs of Alzheimer's disease.