Design of Artificial Neural Networks for Early Detection of Dementia Risk Using Mini-Mental State of Examination (MMSE)

Abstract

Dementia is a brain disorder that interferes with a person's cognitive and behavioral abilities. Unfortunately, late diagnosis is common. Detection of people with suspected dementia in any setting requires a brief and simple test such as MMSE which has become the best‐known and the most often used short screening dementia tool. The aim of this study was to determine the diagnostic accuracy of MMSE screening tool based digital system. We used data from the OASIS Longitudinal dataset provided by the University of Washington Center for Alzheimer's Disease Research, Randy Buckner of the Howard Hughes Institute of Medicine at Harvard University, the Neuroinformatics Research Group at the University of Washington School of Medicine, and the Biomedical Informatics Research Network. The dataset was processed through training and testing using an artificial neural network architecture. From this data, a machine learning training process was carried out and obtained diagnostic accuracy of 78%. Thus, The early detection of dementia using MMSE tool based digital system is suitable for use as a dementia risk screening process tool in a clinical setting. Further studies are needed to assess diagnostic accuracy using primary data.