Biomedical data analytics and visualisation-a methodological framework

Biomedical data analytics have become a major decision-making aid for the diagnosis and treatment of diseases. Computational and visual analytics enable effective exploration and making sense of large and complex data through the deployment of appropriate machine learning and statistical analytics methods, meaningful visualisation, and human-information interaction. This chapter serves as a tutorial that provides guidelines, discussion, and reviews on methods and technologies that have been used for biomedical data analytics. We discuss the major processes of biomedical data analytics that are required to produce effective analytical outcomes. The chapter covers comprehensive discussions on computational analytics strategies, including feature selection, feature extraction, and clustering. Methods and several aspects of visual analytics and interactive visualisation in biomedical data analytics are also thoroughly explained and illustrated, including scatter plots, heat maps, parallel coordinates, network and graph visualisations, tailored visualisation, and visualisation in emerging technologies (such as virtual reality and augmented reality), as well as the human aspect of visualisation.