Random forest prediction of Alzheimer's disease using pairwise selection from time series data

TL;DR

This paper presents a machine learning approach using pairwise time series data to predict Alzheimer's disease, effectively handling irregular sampling and missing data, and achieving competitive accuracy in diagnosis and prognosis.

Contribution

Introduces a novel pairwise time series method for Alzheimer's prediction that manages irregular data and combines demographic with non-time-varying variables.

Findings

Achieved an mAUC of 0.82 for diagnosis

Classification accuracy of 0.73 for Alzheimer's prediction

Method is comparable to existing approaches

Abstract

Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a machine learning method to learn the relationship between pairs of data points at different time separations. The input vector comprises a summary of the time series history and includes both demographic and non-time varying variables such as genetic data. The dataset used is from the 2017 TADPOLE grand challenge which aims to predict the onset of Alzheimer's disease using including demographic, physical and cognitive data. The challenge is a three-fold diagnosis classification into AD, MCI and control groups, the prediction of ADAS-13 score and the normalised ventricle volume. While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy of 0.73. The results show that the method is effective and comparable with other methods.