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Intuitionistic Fuzzy Variation Analysis with Replication in Prostate Cancer Research
Velichka Traneva and Stoyan Tranev
The variation analysis (ANOVA) can examine the impact of one or more factors on the development of the disease. When medical data is vague, it is necessary to extend the traditional tools for analysis of the medical data. Intuitionistic fuzzy sets (IFSs) and index matrices (IMs) are tools for storing and analyzing a large set of data. As a part of the development of an intelligent medical system for predicting the aggressiveness of the disease, we propose in the work an extension of replicated ANOVA, based on the theories of IFSs and IMs, which we refer to as RIFANOVA. We also analyze a unique set of data for 107 patients at a hospital in Bulgaria, tested for prostate cancer, to determine the influence of “prostate-specific antigen (PSA)” and “digital rectal examination (DRE)” factors on the aggressiveness of cancer according to the Gleason scale applying the ANOVA and the RIFANOVA.
Keywords: Index matrices, intuitionistic fuzzy logic, prostate cancer, replicated variation analysis