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Diagnosis System for Predicting Bladder Cancer Recurrence Using Association Rules and Decision Trees

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Book cover Intelligent Systems and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 650))

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Abstract

In this work we present two methods based on Association Rules (ARs) for the prediction of bladder cancer recurrence. Our objective is to provide a system which is on one hand comprehensible and on the other hand with a high sensitivity. Since data are not equitably distributed among the classes and since errors costs are asymmetric, we propose to handle separately the cases of recurrence and those of no-recurrence. ARs are generated from each training set using an associative classification approach. The rules’ uncertainty is represented by a confidence degree. Several symptoms of low intensity can be complementary and mutually reinforcing. This phenomenon is taken into account thanks to aggregate functions which strengthen the confidence degrees of the fired rules. The first proposed classification method uses these ARs to predict the bladder cancer recurrence. The second one combines ARs and decision tree: the original base of ARs is enriched by the rules generated from a decision tree. Experimental results are very satisfactory, at least with the AR’s method. The sensibility rates are improved in comparison with some other approaches. In addition, interesting extracted knowledge was provided to oncologists.

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Notes

  1. 1.

    Sensitivity: the proportion of positive class instances that are correctly classified (called True Positive rate or TP rate).

  2. 2.

    Rabta: University Hospital in Tunis, Tunisia.

  3. 3.

    RTU: Trans-Urethral Resection of bladder cancer, it is an operation which consists in removing one or more tumors on the level of the bladder.

  4. 4.

    BCG (Bacillus Calmette-Guerin): the BCG therapy is a non-specific treatment of localized, non-invasive cancer of the bladder. BCG solution contains alive but little virulent bacteria which have the effect of stimulating the immune system to destroy cancer cells in the bladder.

  5. 5.

    Cystectomy: examination of the interior of the bladder using an optical system.

  6. 6.

    http://www.cs.waikato.ac.nz/ml/index.html.

  7. 7.

    CBA: Classification Based on Association.

  8. 8.

    CMAR: Classification based on Multiple Association Rules.

  9. 9.

    Heterogeneous rules: rules that do not have the same attributes in their premises.

  10. 10.

    TP: True Positive rate.

  11. 11.

    TN: True Negative rate.

  12. 12.

    FP: False Positive rate.

  13. 13.

    FN: False Negative rate.

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Authors and Affiliations

  1. Institut Supérieur d’Informatique, Université de Tunis El Manar, LIPAH, Tunis, Tunisia

    Amel Borgi & Safa Ounallah

  2. Faculté des Sciences de Tunis, Laboratoire de Génétique Immunologie et Pathologies Humaines, Université de Tunis El Manar, Tunis, Tunisia

    Nejla Stambouli, Sataa Selami & Amel Ben Ammar Elgaaied

Authors
  1. Amel Borgi
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  2. Safa Ounallah
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  3. Nejla Stambouli
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  4. Sataa Selami
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  5. Amel Ben Ammar Elgaaied
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Correspondence to Amel Borgi .

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Editors and Affiliations

  1. Faculty of Computing and Engineering, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

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Borgi, A., Ounallah, S., Stambouli, N., Selami, S., Elgaaied, A.B.A. (2016). Diagnosis System for Predicting Bladder Cancer Recurrence Using Association Rules and Decision Trees. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. Studies in Computational Intelligence, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-33386-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-33386-1_3

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