3D Regression Heat Map Analysis of Population Study Data

Klemm, Paul; Lawonn, Kai; Glaßer, Sylvia; Niemann, Uli; Hegenscheid, Katrin; Völzke, Henry; Preim, Bernhard

doi:10.1109/TVCG.2015.2468291

by Paul Klemm, Kai Lawonn, Sylvia Glaßer, Uli Niemann, Katrin Hegenscheid, Henry Völzke, Bernhard Preim

Abstract:

Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease.

Reference:

3D Regression Heat Map Analysis of Population Study Data (Paul Klemm, Kai Lawonn, Sylvia Glaßer, Uli Niemann, Katrin Hegenscheid, Henry Völzke, Bernhard Preim), In IEEE transactions on visualization and computer graphics, volume 22, 2016.

Bibtex Entry:

@article{klemm_3d_2016,
	title = {3D {Regression} {Heat} {Map} {Analysis} of {Population} {Study} {Data}},
	volume = {22},
	issn = {1941-0506 1077-2626},
	doi = {10.1109/TVCG.2015.2468291},
	abstract = {Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease.},
	language = {eng},
	number = {1},
	journal = {IEEE transactions on visualization and computer graphics},
	author = {Klemm, Paul and Lawonn, Kai and Glaßer, Sylvia and Niemann, Uli and Hegenscheid, Katrin and Völzke, Henry and Preim, Bernhard},
	month = jan,
	year = {2016},
	pmid = {26529689},
	keywords = {*Computer Graphics, *Epidemiologic Methods, Breast Neoplasms/epidemiology, Computational Biology/*methods, Fatty Liver, Female, Humans, Imaging, Male, Middle Aged, Three-Dimensional/*methods},
	pages = {81--90}
}