Classification Models for Medical Data with Interpretative Rules
28th International Conference on Neural Information Processing, ICONIP 2021
; 13108 LNCS:227-239, 2021.
Article
in English
| Scopus | ID: covidwho-1599326
ABSTRACT
The raging of COVID-19 has been going on for a long time. Thus, it is essential to find a more accurate classification model for recognizing positive cases. In this paper, we use a variety of classification models to recognize the positive cases of SARS. We conduct evaluation with two types of SARS datasets, numerical and categorical types. For the sake of more clear interpretability, we also generate explanatory rules for the models. Our prediction models and rule generation models both get effective results on these two kinds of datasets. All explanatory rules achieve an accuracy of more than 70%, which indicates that the classification model can have strong inherent explanatory ability. We also make a brief analysis of the characteristics of different rule generation models. We hope to provide new possibilities for the interpretability of the classification models. © 2021, Springer Nature Switzerland AG.
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Database:
Scopus
Language:
English
Journal:
28th International Conference on Neural Information Processing, ICONIP 2021
Year:
2021
Document Type:
Article
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