Fuzzy relation based modeling for medical diagnostic decision support: Case studies

Article type: Research Article

Authors: Wagholikar, Kavishwar B.^{a; *} | Deshpande, Ashok W.^b

Affiliations: [a] Interdisciplinary School of Scientific Computing, University of Pune, Pune 411007, India | [b] University of Pune, India

Correspondence: [*] Corresponding author. E-mail: [email protected]

Abstract: This paper investigates a variation to Adlassnig's fuzzy relation based model for medical diagnosis. The proposed model is an attempt to closely replicate a physician's perceptions of symptom-disease associations and his approximate-reasoning for diagnosis. For proof of principle, the algorithm is evaluated in two sample studies. First case study relates to selected cardiovascular diseases, wherein the required parameters are estimated by interviewing physicians, and an evaluation is performed on a dataset of 79 cases. In the second study, the algorithm is implemented using an alternative semiautomatic approach for a more complex problem of diagnosing common infectious diseases, wherein the parameters are derived from a dataset of 92 case records; for evaluation, jack-knife is performed along with a comparison with Independence Bayes, considered here as the reference standard. The proposed algorithm was found to be as accurate as Independence Bayes for diagnosing common infectious diseases from the small dataset. This result may indicate the utility of proposed algorithm to optimally model the diagnostic process for small datasets; especially, due to its computational simplicity. Further studies on a variety of datasets are needed to establish such a utility.

DOI: 10.3233/KES-2008-125-602

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 12, no. 5-6, pp. 319-326, 2008