Early alzheimer’s disease prediction in machine learning setup: Empirical analysis with missing value computation

Abstract

Alzheimer’s Disease (AD) is the most prevalent progressive neurodegenerative disorder of the elderly. Prospective treatments for slowing down or pausing the process of AD require identification of the disease at an early stage. Many patients with mild cognitive impairment (MCI) may eventually develop AD. In this study, we evaluate the significance of using longitudinal data for efficiently predicting MCI-to-AD conversion a few years ahead of clinical diagnosis. The use of longitudinal data is generally restricted due to missing feature readings. We implement five different techniques to compute missing feature values of neuropsychological predictors of AD. We use two different summary measures to represent the artificially completed longitudinal features. In a comparison with other recent techniques, our work presents an improved accuracy of 71.16% in predicting pre-clinical AD. These results prove feasibility of building AD staging and prognostic systems using longitudinal data despite the presence of missing values.