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<p>A decision tree represents one of the most used data analysis methods for classification tasks. The generated decision models can be visualized as a graph, but this visualization is quite complicated for a domain expert to understand in large or heterogeneous data. Our previous experience with medical data analytics related to the classification of patients with Metabolic Syndrome, Mild Cognitive Impairment, heart disease, or Frailty motivated us to evaluate the potential of new visualizations for this decision model in the medical domain. We managed a user study to design and implement a decision support system containing selected methods to improve the interpretability of the generated tree-based decision model. We hypothesized that this approach would result in more effective communication between data analysts and medical experts, reduce necessary time and energy and bring more comprehensive results. For this purpose, we selected two model-agnostic methods, LIME and SHAP, and one new interactive visualization called Sunburst. We used two data samples for design and evaluation: the publicly available heart disease dataset and the Metabolic Syndrome dataset the participating medical expert provided. We will use the collected feedback and experience for further improvements, like more evaluation metrics related to the usability of the decision models.</p>
</abstract>

A decision tree represents one of the most used data analysis methods for classification tasks. The generated decision models can be visualized as a graph, but this visualization is quite complicated for a domain expert to understand in large or heterogeneous data. Our previous experience with medical data analytics related to the classification of patients with Metabolic Syndrome, Mild Cognitive Impairment, heart disease, or Frailty motivated us to evaluate the potential of new visualizations for this decision model in the medical domain. We managed a user study to design and implement a decision support system containing selected methods to improve the interpretability of the generated tree-based decision model. We hypothesized that this approach would result in more effective communication between data analysts and medical experts, reduce necessary time and energy and bring more comprehensive results. For this purpose, we selected two model-agnostic methods, LIME and SHAP, and one new interactive visualization called Sunburst. We used two data samples for design and evaluation: the publicly available heart disease dataset and the Metabolic Syndrome dataset the participating medical expert provided. We will use the collected feedback and experience for further improvements, like more evaluation metrics related to the usability of the decision models.

Decision Support System Improving the Interpretability of Generated Tree-Based Models

Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics

Department of Internal Medicine, Family Medicine and the History of Medicine, Faculty of Medicine

Department of Public Health, Faculty of Dental Medicine

Acta Electrotechnica et Informatica

This work is licensed under the Creative Commons Attribution 4.0 International License.

A decision tree represents one of the most used data analysis methods for classification tasks. The generated decision models can be visualized as a graph, but...

Decision Support System Improving the Interpretability of Generated Tree-Based Models

Online veröffentlicht: 12. Aug. 2022

Seitenbereich: 27 - 33

Eingereicht: 26. Apr. 2022

Akzeptiert: 30. Juni 2022

DOI: https://doi.org/10.2478/aei-2022-0009

Schlüsselwörter
decision tree, interpretability, decision support

© 2022 Diana Klimonová et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Decision Support System Improving the Interpretability of Generated Tree-Based Models

Online veröffentlicht: 12. Aug. 2022

Seitenbereich: 27 - 33

Eingereicht: 26. Apr. 2022

Akzeptiert: 30. Juni 2022

DOI: https://doi.org/10.2478/aei-2022-0009

Schlüsselwörterdecision tree, interpretability, decision support

© 2022 Diana Klimonová et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
decision tree, interpretability, decision support