Stroke data analysis through a HVN visual mining platform

Huang, Mao Lin1; Yue, Zhixiong1,2; Nguyen, Quang Vinh1; Liang, Jie3; Luo, Zongwei2

10.1109/IV-2.2019.00010

2019

1550-6037

2375-0138

978-1-7281-2851-1

23rd International Conference in Information Visualization - Part II, IV-2 2019

1-6

16-19 July 2019

Adelaide, SA, Australia

10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA

Institute of Electrical and Electronics Engineers Inc.

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.
© 2019 IEEE.

multi-dimensional data visualization visual analytics data mining risk prediction stroke

其他

英语

EI ; CPCI-S

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems

WOS:000538679200001

20193907463261

Behavioral research ; Clustering algorithms ; Data Analytics ; Data mining ; Decision making ; Information systems ; Large dataset ; Mammals ; Risk assessment ; Visualization

Data Processing and Image Processing:723.2 ; Information Science:903 ; Management:912.2 ; Accidents and Accident Prevention:914.1 ; Social Sciences:971

EV Compendex

1.University of Technology, Sydney, Australia
2.Southern University of Science and Technology, China
3.University of Western Sydney, Australia

Huang, Mao Lin,Yue, Zhixiong,Nguyen, Quang Vinh,et al. Stroke data analysis through a HVN visual mining platform[C]. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA:Institute of Electrical and Electronics Engineers Inc.,2019:1-6.