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A semiautomatic method for obtaining a predictive deep learning model and a rule-based system for abdominal aortic aneurysms

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Abstract

Development in the medical field is getting fast every day. People’s interest in improving their expectancy of life, their life quality, and the significant investments in medical laboratories modify the diagnosis methods, application protocols, and surgical techniques. One of the most significant milestones in the medical field have been incorporating computers to improve data analysis during the last years. Nowadays, it is a fact that computers can help physicians, i.e., the use of artificial intelligence techniques. This paper proposes a multistage prediction-based approach and a rule-based system for the treatment of abdominal aortic aneurysms. The first step is to develop a neural network model to predict 30-day mortality during and after aortic endovascular procedures. The second step aims to infer a rule-based system from the previous model. The results show that with only eight features the final predictive model can obtain an accuracy of around 87%. Furthermore, a decision tree with the same accuracy can be inferred from this model using three features and four rules

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Notes

  1. https://nlp.stanford.edu/sentiment/treebank.html

  2. http://www.clipsrules.net/

  3. http://hurlab.med.und.edu/SciMiner/

  4. https://pandas.pydata.org/

  5. https://numpy.org/

  6. https://scikit-learn.org/

  7. https://impyute.readthedocs.io/en/latest/

  8. https://github.com/epsilon-machine/missingpy

  9. https://imbalanced-learn.readthedocs.io/en/stable/

  10. https://keras.io/

  11. https://github.com/slundberg/shap

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Acknowledgements

In the data collection of this project, several people have collaborated: A. Montes, R. Conejero, R. R. Carvajal, R. Lainez, T. H. Carbonell, M. Canalejo, F. Manresa, A. Moreno, L. Gómez Pizarro, A.R. Morata, J.P. Reyes, J. Cuenca, I. Rastrollo, J. Fernández Herrera, E. Herrero, R. Yoldi.Also special thanks to C.R. Garrido, from FIBAO, for the statistical design and data analysis of the records. Finally, we also want to thank Fernando Pérez Lara for his help at some points of the experimentation.

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Authors and Affiliations

  1. CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón, 28223, Madrid, Spain

    Alberto Nogales, Miguel Pajares & Álvaro J. García-Tejedor

  2. Department Angiology and Vascular Surgery, Hospital Quirónsalud Marbella, Marbella, Spain

    Fernando Gallardo

  3. Department Angiology and Vascular Surgery, Complejo Hospitalario de Jaén, Jaén, Spain

    Javier Martinez Gamez

  4. Department Angiology and Vascular Surgery, Hospital Universitario San Cecilio, Granada, Spain

    José Moreno

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Table 6 Features in EVAR-30D SACVA registry
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Nogales, A., Gallardo, F., Pajares, M. et al. A semiautomatic method for obtaining a predictive deep learning model and a rule-based system for abdominal aortic aneurysms. J Intell Inf Syst 61, 651–671 (2023). https://doi.org/10.1007/s10844-023-00781-5

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