DEMENTIA DETECTION USING MACHINE LEARNING

Abstract

Dementia is a progressive neurodegenerative disease characterized by cognitive decline that affects 
millions of people worldwide. Early detection of dementia is critical for timely intervention and better 
patient outcomes. In recent years, machine learning techniques have shown promise in detecting dementia 
by analysing different types of patient data, including cognitive assessments, neuroimaging and genetic 
information. This article provides a comprehensive overview of machine learning methods for dementia 
detection, highlighting the main methods, datasets and performance metrics used in recent studies. We 
discuss the challenges and opportunities of using machine learning in early detection. In addition, this 
paper highlights the potential of machine learning to facilitate early diagnosis and individualized treatment 
strategies for people at risk of dementia. An analysis of critical importance to identify key factors 
influencing dementia classification. This analysis provides insight into factors associated with the 
progression of the disease. The implications of our research extend to health care as it provides a robust 
tool for early detection of dementia that can be integrated into clinical practice.