J Med Syst - A novel mathematical approach to diagnose premenstrual syndrome.

Tópicos

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Resumo

Diagnosis of Premenstrual syndrome (PMS) is a research challenge due to its subjective presentation. An undiagnosed PMS case is often termed as 'borderline' ('B') that further add to the diagnostic fuzziness. This study proposes a methodology to diagnose PMS cases using a combined knowledge engineering and soft computing techniques. According to the guidelines of American College of Gynecology (ACOG), ten symptoms have been selected and technically processed for 50 cases each having class labels-'B' or 'NB' (not borderline) using domain expertise. Any Attribute that fails normality test has been excluded from the study. Decision tree (DT) has then been induced in obtaining the initial class boundaries and mining the important Attributes to classify PMS cases. Prior doing so, the best split criterion has been set using the maximum information gain measure. Initial information about classification boundaries are finally used to measure fuzzy membership values and the corresponding firing strengths have been measured for final classification of PMS 'B' cases.

Resumo Limpo

diagnosi premenstru syndrom pms research challeng due subject present undiagnos pms case often term borderlin b add diagnost fuzzi studi propos methodolog diagnos pms case use combin knowledg engin soft comput techniqu accord guidelin american colleg gynecolog acog ten symptom select technic process case class labelsb nb borderlin use domain expertis attribut fail normal test exclud studi decis tree dt induc obtain initi class boundari mine import attribut classifi pms case prior best split criterion set use maximum inform gain measur initi inform classif boundari final use measur fuzzi membership valu correspond fire strength measur final classif pms b case

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