A novel mathematical approach to diagnose premenstrual syndrome.

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.