Predicting progression of Alzheimer’s disease using new survival analysis approach
Date
2024-01
Journal Title
Journal ISSN
Volume Title
Type
Article
Publisher
Institute of Advanced Engineering and Science (IAES)
Series Info
Indonesian Journal of Electrical Engineering and Computer Science;Vol. 33, No. 1, January 2024, pp. 603∼611
Scientific Journal Rankings
Abstract
It is critical to determine the risk of Alzheimer’s disease (AD) in people with
mild cognitive impairment (MCI) to begin treatment early. Its development is
affected by many things, but how each effect and how the disease worsens is
unclear. Nevertheless, an in-depth examination of these factors may provide
a reasonable estimate of how long it will take for patients at various stages of
the disease to develop Alzheimer’s. Alzheimer’s disease neuroimaging initiative (ADNI) database had 900 people with 63 features from magnetic resonance
imaging (MRI), genetic, cognitive, demographic, and cerebrospinal fluid data.
These characteristics are used to track AD progression. A hybrid approach for
dynamic prediction in clinical survival analysis has been developed to track progression to AD. The method uses a random forest cox regression approach to
figure out how long it will take for MCI to turn into AD. In order to evaluate
the result concordance index is used. The concordance index measures the rank
correlation between predicted risk scores and observed time points. The concordance index was statistically considerably higher in the suggested work than in
previous approaches with a score of 95.3%, which is higher than others.
Description
Keywords
Alzheimer’s disease; MCI to AD; Prediction; Progression; Survival analysis