A Hybrid Model- and Memory-Based Collaborative Filtering Algorithm for Baseline Data Prediction of Friedreich's Ataxia Patients

Abstract

Friedreich's ataxia (FRDA) is the most common inherited ataxia that causes progressive damage of nervous systems and performance deterioration of physical movements. FRDA baseline data analysis plays a crucial role in advancing the disease research, where the main obstacle comes from the baseline data collection primarily due to the degenerative symptoms of the FRDA patients. Inspired by the nowadays popular collaborative filtering (CF) method, a new FRDA baseline data collection algorithm is proposed in this article, with which the patients (or their families) are only required to provide certain reliable baseline data acquired from home and the uncertain/missing parts of the data can then be predicted with acceptable accuracy by utilizing existing patient information. The framework of the proposed algorithm is constructed based on a novel hybrid model combining the merits of model- and memory-based CF methods, thereby facilitating the baseline data collection with improved prediction accuracy. The proposed hybrid algorithm exhibits the following two main features: when a patient does not have neighbors sharing similar baseline data, the model-based CF component is activated to employ certain clustering method to find similar neighbors based on their attributes; and in the case that a patient does have neighbors, a novel similarity measure, which accounts for more statistical characteristics by integrating rating habits and degree of co-rated items, is developed in the memory-based component of the algorithm in order to adjust initial similarities between the patients. To evaluate the advantages of the proposed algorithm, the Scale for the Assessment and Rating of Ataxia is selected from the European FRDA Consortium for Translational Studies database. Experimental results demonstrate that our proposed hybrid CF approach is superior to other conventional approaches.