| dc.description.abstract | Dementia represents a significant public health issue, characterized by cognitive decline
that disrupts daily activities and the ability to live independently. With the global
population aging, the number of individuals affected by dementia is expected to rise,
necessitating innovative approaches for diagnosis and care. This study addresses the
critical need for early detection of dementia, which is essential for timely treatments
that enhance patient outcomes and improve the quality of life for both patients and
caregivers. This research explores the integration of artificial intelligence (AI) into
knowledge management systems, leveraging machine learning (ML) and deep learning
(DL) techniques to analyze multimodal data from diverse sources, including electronic
health records, genetic information, and lifestyle factors. Using a systematic review
methodology, the study synthesizes existing literature on AI-driven approaches for
dementia detection, highlighting their effectiveness in identifying risk factors and
early symptoms. The analysis compares various AI techniques, such as Convolutional
Neural Networks (CNNs) for image analysis and Natural Language Processing (NLP)
for symptom extraction. Findings reveal that integrating diverse data sources such as
clinical, behavioural, and neuroimaging significantly enhances the accuracy of early
dementia detection. AI technologies are shown to uncover complex patterns and
connections that traditional diagnostic methods often overlook, thereby improving
diagnostic precision and patient outcomes. Future advancement may include real-time
monitoring via wearable technology and enhanced multimodal data integration to
refine predictive models. These developments hold potential for addressing the growing
burden of dementia and improving patient care. | en_US |
