A Collaborative-Filtering-Based Data Collection Strategy for Friedreich’s Ataxia

Abstract

Friedreich’s ataxia (FRDA) is an inherited neurodegenerative disorder with the prevalence of 2–4 in every 100,000 Caucasian population. Since 2010, the European Friedreich’s Ataxia Consortium for Translational Studies (EFACTS) has endeavored to define and characterize FRDA by recruiting over 940 FRDA patients to provide baseline data in 19 study sites distributed in 9 European countries. It is challenging to collect primary data at EFACTS’ study sites because of physical/psychological difficulties in recruiting new patients and collecting follow-up assessment data. To overcome such challenges, in this paper, we propose a novel data collection strategy for the FRDA baseline data by using the collaborative filtering (CF) approaches. This strategy is motivated by the popularity of the nowadays “Recommendation System” whose central idea is based on the fact that similar patients have similar symptoms on each test item. By doing so, instead of having no data at all, the FRDA researchers would be provided with certain predicted baseline data on patients who cannot attend the assessments for physical/psychological reasons, thereby helping with the data analysis from the researchers’ perspective. It is shown that the CF approaches are capable of predicting baseline data based on the similarity in test items of the patients, where the prediction accuracy is evaluated based on three rating scales selected from the EFACTS database. Experimental results demonstrate the validity and efficiency of the proposed strategy.