Artificial Intelligence Based Alzheimer’s Disease Detection and Treatment Recommendation System

Abstract

The significance of the use of Artificial Intelligence techniques towards early diagnosis, detection, and treatment of Alzheimer’s disease (AD) is highlighted in this research work. This work proposes an inclusive AI system that combines several significant aspects: MRI analysis for detecting and staging early on the disease, emotion recognition and response analysis, risk assessment, and memory therapy sessions. The system employs advanced deep learning algorithms to read MRIs to diagnose structural changes within the most vital regions of the brain including the hippocampus, temporal lobe, frontal lobe parietal lobe. The system also has emotion recognition abilities via facial expression analysis and voice data analysis provides enough answers to ensure emotional well-being among the patients. The analysis is done by machine learning. algorithms that are exposed to the handling of data from various Data sources like demographic data, medical history, cognitive test scores, and lifestyle traits. The system also has specialized memory treatment exercises such as the memorization sequence games and picture recall to enhance cognitive skills Cities. In prediction cases on a patient’s condition, suitable treatment recommendations and therapy adjustments are provided by the system. Secondly, the system contains elaborate tracking and reporting features that allow healthcare professionals to monitor patient improvement and adjust interventions accordingly. The study highlights the vast advantages and potential achieved through employing this AI-based diagnostic system, in addition to the challenges and potential disruptions which can affect its successful implementation in healthcare environments. The potential for future enhancement of the system’s functionality is also addressed in this study.