Healthcare Big Data Voice Pathology Assessment Framework

Abstract

The fast-growing healthcare big data plays an important role in healthcare service providing. Healthcare big data comprise data from different structured, semi-structured, and unstructured sources. These data sources vary in terms of heterogeneity, volume, variety, velocity, and value that traditional frameworks, algorithms, tools, and techniques are not fully capable of handling. Therefore, a framework is required that facilitates collection, extraction, storage, classification, processing, and modeling of this vast heterogeneous volume of data. This paper proposes a healthcare big data framework using voice pathology assessment (VPA) as a case study. In the proposed VPA system, two robust features, MPEG-7 low-level audio and the interlaced derivative pattern, are used for processing the voice or speech signals. The machine learning algorithms in the form of a support vector machine, an extreme learning machine, and a Gaussian mixture model are used as the classifier. In the experiments, the proposed VPA system shows its efficiency in terms of accuracy and time requirement.

Keywords

• Healthcare big data
• voice pathology
• classification
• feature extraction