Predicting progression of Alzheimer’s disease using new survival analysis approach

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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

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.

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Keywords

Alzheimer’s disease; MCI to AD; Prediction; Progression; Survival analysis

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