Stroke Data Analysis through a HVN Visual Mining Platform

• Published in: 2019 23rd International Conference in Information Visualization – Part II pp. 1 - 6
• Main Authors: Huang, Mao Lin, Yue, Zhixiong, Nguyen, Quang Vinh, Liang, Jie, Luo, Zongwei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2019
• Subjects: Clustering algorithms; Data mining; Data visualization; decision making; Diseases; Multidimensional data visualization; risk prediction; Stroke (medical condition); stroke data; Visual Data Analytics; Visual Data Mining; Visualization
• DOI: 10.1109/IV-2.2019.00010

Today there are abounding collected data in cases of various diseases in medical sciences. Physicians can access new findings about diseases and procedures in dealing with them by probing these data. Clinical data is a collection of large and complex datasets that commonly appear in multidimensional data formats. It has been recognized as a big challenge in modern data analysis tasks. Therefore, there is an urgent need to find new and effective techniques to deal with such huge datasets. This paper presents an application of a new visual data mining platform for visual analysis of the stroke data for predicting the levels of risk to those people who have the similar characteristics of the stroke patients. The visualization platform uses a hierarchical clustering algorithm to aggregate the data and map coherent groups of data-points to the same visual elements - curved 'super-polylines' that significantly reduces the visual complexity of the visualization. On the other hand, to enable users to interactively manipulate data items (super-polylines) in the parallel coordinates geometry through the mouse rollover and clicking, we created many 'virtual nodes' along the multi-axis of the visualization based on the hierarchical structure of the value range of selected data attributes. The experimental result shows that we can easily verify research hypothesis and reach to the conclusion of research questions through human-data & human-algorithm interactions by using this visual platform with a fully transparency manner of data processing.