Malik, D. S.

Previously, the biological factors were weighed by the expert opinions. In this paper, we evaluate the risk factors proposed by the expert opinions. We use fuzzy logic techniques for predicting risk for development of carotid intima–medial thickness (CIMT) using expert opinion and various factors associated with CIMT in recently menopausal women. Study participants were enrolled in the Kronos Early Estrogen Prevention Study (KEEPS), a four-year study where participants were assigned to either oral or transdermal hormone treatments or to placebo. We use preference modeling techniques to determine the consensus winner of the causal factors. We use a measure to determine the degree to which one factor is preferred to another. We also determine the extent to which a factor is preferred to another by "most" experts. We determine the degree to which the experts agree. We consider a model concerning social networks and the effects of these networks on persons' opinions.

Carotid intima-medial thickness (CIMT) allows for early detection of carotid atherosclerotic vascular disease. Using fuzzy logic techniques, we propose a model for calculating the risk for predicting CIMT using expert opinion and various factors associated with CIMT in recently menopausal women. Study participants were enrolled in the Kronos Early Estrogen Prevention Study (KEEPS), a four year study where participants were assigned to either oral or transdermal hormone therapy or to the placebo group. The fuzzy mathematical model uses five fuzzy logic methods to calculate this risk factor for CIMT, including the analytic hierarchy process (AHP) method, Guiasu method, Yen method, Dempster–Shafer (DS) method, and the set valued statistical method (SVSM). The four methods, AHP, Guiasu, Yen, DS, and SVSM, were correlated to each other according to the Pearson ranking method. In addition, results showed that the estrogen treatment groups showed a statically significant decrease in calculated CIMT risk compared to placebo when all the methods were combined together (p = 0.0017). Our fuzzy mathematical model provides the first fuzzy mathematical model to predict the risk of CIMT in recently menopausal women and menopausal hormone therapy may be beneficial in reducing this risk.