Mixed Fuzzy Clustering for Deriving Predictive Models in Intensive Care Units

Cátia M. Salgado (), Susana M. Vieira () and João M. C. Sousa ()
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Cátia M. Salgado: Universidade de Lisboa
Susana M. Vieira: Universidade de Lisboa
João M. C. Sousa: Universidade de Lisboa

Chapter Chapter 4 in Operations Research Applications in Health Care Management, 2018, pp 81-99 from Springer

Abstract: 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.

Date: 2018
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DOI: 10.1007/978-3-319-65455-3_4

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