6533b827fe1ef96bd1287107

RESEARCH PRODUCT

Comparison between statistical and fuzzy approaches for improving diagnostic decision making in patients with chronic nasal symptoms

Valerio LacagninaGaia La PortaGabriele Di LorenzoMaria Stefania Leto-baroneGiuseppe PingitoreSimona La Piana

subject

medicine.medical_specialtyLogistic regression modelSettore MED/09 - Medicina InternaSkin prick testLogicFuzzy inference systemFuzzy modelPrimary careSettore MED/10 - Malattie Dell'Apparato RespiratorioFuzzy relationLogistic regressionMachine learningcomputer.software_genreFuzzy logicSettore SECS-S/06 -Metodi Mat. dell'Economia e d. Scienze Attuariali e Finanz.Artificial IntelligenceFuzzy modelmedicineIn patientMathematicsNasal symptombusiness.industryApproximate reasoningTest (assessment)Data setPhysical therapyArtificial intelligenceDiagnostic decision makingbusinesscomputerNasal symptoms

description

This paper compares a fuzzy model, expressed in rule-form, with a well known statistical approach (i.e. logistic regression model) for diagnostic decision making in patients with chronic nasal symptoms. The analyses were carried out using a database obtained from a questionnaire administered to 1359 patients with nasal symptoms containing personal data, clinical data and skin prick test (SPT) results. Both the fuzzy model and the logistic regression model developed were validated using a data set obtained from another medical institution. The accuracy of the two models in identifying patients with positive or negative SPT was similar. This study is a preliminary step to the creation of a software that primary care doctors can use to make a diagnostic decision, when deciding whether patients with nasal symptoms need allergy testing or not.

yearjournalcountryeditionlanguage
2014-02-01Fuzzy Sets and Systems
https://doi.org/10.1016/j.fss.2013.10.013