Proposing a new methodology on vague association rule mining for the diagnosis of heart disease hesitation patterns - Umasankar, P.; Thiagarasu, V. (International Journal of Engineering and Technology, 2019)
In the realistic situation, the health care has which contain imprecisely specified data. This imprecise data indicates the presence of vagueness, incompleteness and uncertainty which causes the problem during important decision-making task in the prediction of heart disease. Traditional Association Rule Mining has limitations as it only deals with the features that are actually present in the prediction of heart disease and ignores the features that are almost not considered for the heart disease prediction. Furthermore, these features may be placed with predictive feature by imposing its attractiveness measure; disease prediction pattern mining in the scenario of imprecise and vague environment is very difficult which is frequent in recent years. For the effectiveness of retrieved hesitated patterns and rules, the concept of vague set theory is used. For the same consideration, heart disease dataset features as weighting factor is used for generation of disease prediction patterns.Â Â Â