Applied Mathematics

Volume 9, Issue 10 (October 2018)

ISSN Print: 2152-7385   ISSN Online: 2152-7393

Google-based Impact Factor: 0.58  Citations  

Fuzzy Modelling for Predicting the Risk of Recurrence and Progression of Superficial Bladder Tumors

HTML  XML Download Download as PDF (Size: 1580KB)  PP. 1091-1103  
DOI: 10.4236/am.2018.910074    1,057 Downloads   2,111 Views  

Affiliation(s)

ABSTRACT

Nowadays, bladder cancer is the fourth most common cancer in adults and the second most frequent urogenital tumor. Predicting recurrence and progression of superficial bladder tumors, with available clinical information to decide the therapy to be used is a difficult task. In this work, two mathematical models were developed to help specialists on the decision process. The mathematical tool used to formulate the model was the fuzzy sets theory, due to its capacity in dealing with uncertainties inherent in medical concepts. In the first model, Stage, Grade and Size of the tumor were also considered input variables and Risk of Recurrence of a superficial bladder tumor as output variable of the first Fuzzy Rule-Based Systems (FRBS). In the second model, in addition to the Stage, Grade and Size of the tumor, it was also considered as input variable of a second FRBS Carcinoma in situ and, the Risk of Progression of superficial tumors as an output variable. For each model, simulations were made with data originated from of patients of the Clinics Hospital/ UNICAMP and A. C. Camargo Hospital of São Paulo, with the aim to verify the reliability of results generated by the two systems. From a database and the possibility found by FRBS, after the possibility-probability transformation, we can generate the real probability of each fuzzy output set.

Share and Cite:

Vendite, L. , Savergnini, K. , Ferreira, U. and Matheus, W. (2018) Fuzzy Modelling for Predicting the Risk of Recurrence and Progression of Superficial Bladder Tumors. Applied Mathematics, 9, 1091-1103. doi: 10.4236/am.2018.910074.

Cited by

No relevant information.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.