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Mixed Fuzzy Clustering for Deriving Predictive Models in Intensive Care Units

In: Operations Research Applications in Health Care Management

Author

Listed:
  • Cátia M. Salgado

    (Universidade de Lisboa)

  • Susana M. Vieira

    (Universidade de Lisboa)

  • João M. C. Sousa

    (Universidade de Lisboa)

Abstract

This chapter presents two novel approaches for the identification of Takagi-Sugeno fuzzy models with time variant and time invariant features. The mixed fuzzy clustering (MFC) algorithm is used for determining the parameters of Takagi-Sugeno fuzzy models (FMs) in two different ways: (1) MFC FM, where the antecedent fuzzy sets are determined based on the partition matrix generated by the mixed fuzzy clustering algorithm; (2) FCM–UMFC FM, where the input features are transformed using MFC and the antecedent fuzzy sets are derived using fuzzy c-means (FCM). The fuzzy modeling approaches are tested on four health care applications for the classification of critically ill patients: administration of vasopressors in pancreatitis and pneumonia patients, mortality in septic shock and early readmissions. Both approaches increase the performance of Takagi-Sugeno based on FCM, in all datasets. In particular, the best performer, FCM–UMFC FM, achieves notable improvements in the four datasets.

Suggested Citation

  • Cátia M. Salgado & Susana M. Vieira & João M. C. Sousa, 2018. "Mixed Fuzzy Clustering for Deriving Predictive Models in Intensive Care Units," International Series in Operations Research & Management Science, in: Cengiz Kahraman & Y. Ilker Topcu (ed.), Operations Research Applications in Health Care Management, chapter 0, pages 81-99, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-65455-3_4
    DOI: 10.1007/978-3-319-65455-3_4
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