Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm

Introduction: There is a 99.6% failure rate of clinical trials for drugs to treat Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily identified at early stages. This study investigated machine learning approaches to use clinical data to predict the progression of AD in future years.

Methods: Data from 1737 patients were processed using the "All-Pairs" technique, a novel methodology created for this study involving the comparison of all possible pairs of temporal data points for each patient. Machine learning models were trained on these processed data and evaluated using a separate testing data set (110 patients).

Results: A neural network model was effective (mAUC = 0.866) at predicting the progression of AD, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment.

Discussion: Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics.