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Profiling Using Fuzzy Set QCA for Medical Diagnosis-The Case of Anemia

Authors:
Stavroula Barbounaki
Nikos Dimitrioglou
Dimitris Kardaras
Ilias Petrounias

Keywords: medical diagnosis; intelligent systems; fuzzy logic; QCA; healthcare

Abstract:
Despite the fact that anemia is a common disease, its diagnosis can be elusive. The signs and symptoms of anemia are generally unreliable in predicting the degree of anemia. Its diagnosis is mainly based on information of patient history and results of diagnostic tests that measuring indicators correlated to the disease. This paper suggests an approach to anemia diagnosis for adults by utilizing the fuzzy set Qualitative Comparative Analysis (FsQCA), which is not been used previously in medical diagnosis. The FsQCA, as an extension of QCA, is using fuzzy sets and entails the analysis of necessary and sufficient conditions to produce the some outcome, such as morbidity and severity. This paper aims to produce a set of causal configurations that can be used to assess and diagnose a medical case. The data set is collected from medical data sources. The factors to be considered are physiological indicators, such as hemoglobin, ferritin, mean corpuscular volume and hemoglobin, as well as age, gender and comorbidity. The anemia diagnosis is the outcome set used in this study. The proposed approach is tested for its accuracy and validity.

Pages: 55 to 60

Copyright: Copyright (c) IARIA, 2018

Publication date: November 18, 2018

Published in: conference

ISSN: 2308-4464

ISBN: 978-1-61208-681-1

Location: Athens, Greece

Dates: from November 18, 2018 to November 22, 2018