DM algorithms in health industry

TL;DR

This survey discusses how data mining algorithms in the health industry leverage modern multi-core processors' SIMD and MIMD parallelism, addressing challenges and applications in medical data analysis.

Contribution

It provides an overview of parallel computing approaches for data mining in health applications using commodity multi-core processors.

Findings

Multi-core processors enable parallel data mining in health applications.

Parallelism improves efficiency in processing medical imaging data.

Memory access remains a key challenge for performance.

Abstract

This survey reviews several approaches of data mining (DM) in healthindustry from many research groups world wide. The focus is on modern multi-core processors built into today's commodity computers, which are typically found at university institutes both as small server and workstation computers. So they are deliberately not high-performance computers. Modern multi-core processors consist of several (2 to over 100) computer cores, which work independently of each other according to the principle of "multiple instruction multiple data" (MIMD). They have a common main memory (shared memory). Each of these computer cores has several (2-16) arithmetic-logic units, which can simultaneously carry out the same arithmetic operation on several data in a vector-like manner (single instruction multiple data, SIMD). DM algorithms must use both types of parallelism (SIMD and MIMD), with access to the main memory (centralized component) being the main barrier to increased efficiency. This is important for DM in healthindustry applications like ECG, EEG, CT, SPECT, fMRI, DTI, ultrasound, microscopy, dermascopy, etc.