Early Cognitive Decline Assessment via the Synergy of Machine Learning Algorithms and Cognitive Proficiency Examination

The rapid increase in global aging underscores the urgency of devising efficient strategies for predicting cognitive decline, a precursor to conditions like dementia and Alzheimer's. Current methodologies for detection often rely on a combination of cognitive, clinical, and physical data, with the latter two often necessitating the use of imaging or fluid samples. However, leveraging Electronic Health Records (EHRs) can circumvent these invasive methods, enabling quicker and more efficient diagnostics. This study emphasizes the significance of early detection, especially among middle-aged individuals with chronic conditions such as diabetes and hypertension, both of which have been linked to an increased risk of dementia. The research relies on a dataset sourced from “Data World”, focusing on age cohorts between 40 to 65. This age group is pivotal, as certain Alzheimer's risk factors manifest around this time, and several studies have underscored the connection between middle-age hypertension and late-life dementia. Through a meticulous analysis, involving both traditional cognitive tests and advanced machine learning models like SVM and Deep Neural Networks, this study aims to offer a comprehensive method for early cognitive decline assessment.