A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease
This work addresses the problem of early prediction of Alzheimer's progression for patients and clinicians, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled predicting deterioration in Alzheimer's disease by applying machine learning models to data from the Alzheimer's Disease Neuroimaging Initiative, achieving AUC scores of 0.88 for cognitively normal subjects and 0.76 for those with mild cognitive impairment.
This paper explores deterioration in Alzheimers Disease using Machine Learning. Subjects were split into two datasets based on baseline diagnosis (Cognitively Normal, Mild Cognitive Impairment), with outcome of deterioration at final visit (a binomial essentially yes/no categorisation) using data from the Alzheimers Disease Neuroimaging Initiative (demographics, genetics, CSF, imaging, and neuropsychological testing etc). Six machine learning models, including gradient boosting, were built, and evaluated on these datasets using a nested crossvalidation procedure, with the best performing models being put through repeated nested cross-validation at 100 iterations. We were able to demonstrate good predictive ability using CART predicting which of those in the cognitively normal group deteriorated and received a worse diagnosis (AUC = 0.88). For the mild cognitive impairment group, we were able to achieve good predictive ability for deterioration with Elastic Net (AUC = 0.76).