OBJECTIVES: Mobile
healthcare applications are becoming a growing trend. Also, the
prevalence of
dementia in modern society is showing a steady growing trend. Among degenerative
brain diseases that cause
dementia, Alzheimer disease (AD) is the most common. The purpose of this study was to identify AD
patients using
magnetic resonance imaging in the mobile
environment .
METHODS: We propose an incremental
classification for mobile healthcare systems. Our
classification method is based on incremental
learning for AD
diagnosis and AD prediction using the cortical thickness data and
hippocampus shape. We constructed a classifier based on
principal component analysis and linear
discriminant analysis . We performed initial
learning and mobile subject
classification . Initial
learning is the group
learning part in our server. Our
smartphone agent implements the mobile
classification and shows various results.
RESULTS: With use of cortical thickness
data analysis alone, the discrimination accuracy was 87.33% (
sensitivity 96.49% and
specificity 64.33%). When cortical thickness data and hippocampal shape were analyzed together, the achieved accuracy was 87.52% (
sensitivity 96.79% and
specificity 63.24%).
CONCLUSIONS: In this
paper , we presented a
classification method based on
online learning for AD
diagnosis by employing both cortical thickness data and hippocampal shape
analysis data. Our
method was implemented on
smartphone devices and discriminated AD
patients for normal group.