Resilient parallel similarity-based reasoning for classifying heterogeneous medical cases in MapReduce

Given the exponentially increasing volume of heterogenous medical cases, it is difficult to efficiently perform similarity-based reasoning (SBR) on a centralized machine. In this paper, we investigate how to perform SBR using MapReduce (SBRMR), which is an inference framework for data-intensive applications over clusters of computers. To combine the similarities from the individual machines, a mixed integer optimization problem is formulated to filter the priority reference cases. Besides, a resilient mapping mechanism is employed using a quadratic optimization model for weighting the attributes and making the neighborhoods in the same class compact, hence improving the inference capacity. Our experiments on classifying the medical cases demonstrate that SBRMR has approximately 4.1% improvement in classification accuracy over SBR, which suggests that SBRMR is an efficient and resilient similarity-based inference approach.