Using longitudinal data for predicting Alzheimer’s disease
Understanding the predictors of Alzheimer’s disease will aid early diagnosis.
This study makes use of the DPUK cross-cohort population studies to better predict whether a specified individual will develop Alzheimer’s disease within a given future period. The primary goal is to use machine learning for data analysis to support the efficient design of clinical trials. The prediction will be in the form of a score which represents a likelihood of a positive diagnosis within that period.
The impact of this research is to find patterns that are characteristic of the disease process and use them for prediction and insight into the causes of Alzheimer’s disease.