Decision Support in Medical Data Using 3D Decision Tree Visualisation

Decision making is a difficult task, especially in the medical domain. Decision trees are among the most often used supervised machine learning algorithms, primarily due to their straightforward interpretation through rules, they are composed of. Visualizations of decision trees can help with the understanding of data and decision rules on top of them. This paper proposes a 3D decision tree visualization method that allows effective decision tree exploration and help with decision tree interpretation. The main contribution of this visualization is the ability to display global tree structure and the difference in the distributions of values between subsets created by individual decision nodes. The approach is evaluated using a design study on a typical medical dataset and on a synthetic dataset with controlled properties. It shows the visualization’s ability to display individual components of the decision rule and the ability to see other correlated factors in the decision and the change it causes in subsets of data created by individual decision nodes.

[1]  Shereen M. El-Metwally,et al.  Decision tree classifiers for automated medical diagnosis , 2013, Neural Computing and Applications.

[2]  Gavriel Salvendy,et al.  Design and evaluation of visualization support to facilitate decision trees classification , 2007, Int. J. Hum. Comput. Stud..

[3]  Antonio J. Serrano,et al.  A new visualization tool for data mining techniques , 2016, Progress in Artificial Intelligence.

[4]  Fahad Shahbaz Khan,et al.  Data Mining in Oral Medicine Using Decision Trees , 2008 .

[5]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[6]  Olaf Hellwich,et al.  Colorful Trees: Visualizing Random Forests for Analysis and Interpretation , 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

[7]  Michael J. Bannon,et al.  Opioid Deaths: Trends, Biomarkers, and Potential Drug Interactions Revealed by Decision Tree Analyses , 2018, Front. Neurosci..

[8]  Nada Lavrac,et al.  Data mining and visualization for decision support and modeling of public health-care resources , 2007, J. Biomed. Informatics.

[9]  Tu Bao Ho,et al.  Knowledge visualization in hepatitis study , 2006, APVIS.

[10]  M. Sheelagh T. Carpendale,et al.  Visualization of Uncertainty and Reasoning , 2007, Smart Graphics.

[11]  Richard S. Johannes,et al.  Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus , 1988 .

[12]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[13]  Soukaina Filali Boubrahimi,et al.  Coronal Mass Ejection Data Clustering and Visualization of Decision Trees , 2018 .

[14]  Simon Urbanek.  Visualizing Trees and Forests , 2008 .

[15]  Jarke J. van Wijk,et al.  BaobabView: Interactive construction and analysis of decision trees , 2011, 2011 IEEE Conference on Visual Analytics Science and Technology (VAST).

[16]  Ashwini Venkatasubramaniam,et al.  Branching Out: Use of Decision Trees in Epidemiology , 2018, Current Epidemiology Reports.

[17]  Hsinchun Chen,et al.  Information Visualization for Decision Support , 2008 .

[18]  P. Kokol,et al.  Comprehensive Decision Tree Models in Bioinformatics , 2012, PloS one.

[19]  Ying LU,et al.  Decision tree methods: applications for classification and prediction , 2015, Shanghai archives of psychiatry.

[20]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[21]  Tu Bao Ho,et al.  Interactive Visualization in Mining Large Decision Trees , 2000, PAKDD.

[22]  Xiaohong Jiang,et al.  An intelligent medicine recommender system framework , 2016, 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA).

[23]  Torsten Möller,et al.  TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees , 2018, IEEE Transactions on Visualization and Computer Graphics.

[24]  Alicja Wakulicz-Deja,et al.  Visualization of Rough Set Decision Rules for Medical Diagnosis Systems , 2009, RSFDGrC.

[25]  Wenming Guo,et al.  Applied research on visual mining technology in medical data , 2016, 2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS).

[26]  Huaiyu Zhu.  On Information and Sufficiency , 1997 .