Bleeding predisposition assessments in tonsillectomy/adenoidectomy patients using fuzzy interquartile encoded neural networks.
A fuzzy set theoretic methodology is described that serves as a classification preprocessing strategy for supervised feed-forward neural networks. This methodology, fuzzy interquartile encoding, determines the respective degrees to which a feature belongs to a collection of fuzzy sets that overlap at the respective quartile boundaries of the feature. These membership values are subsequently used in place of the original feature. This transformation has a normalizing effect on the feature space and is more robust to feature outliers. Its effectiveness is scrutinized using several synthetic data sets with various underlying distributions. Fuzzy interquartile encoding is shown to consistently improve the discriminatory power of the underlying classifiers. The methodology is also applied to two biomedical data sets relating to tonsillectomy and/or adenoidectomy patients who may or may not have had a predisposition to excessive bleeding during their operation. The features of the first data set are blood sample test results acquired from a coagulation laboratory and the class labels are one of three hemostatic defects as identified by the reference tests. The second data set consists of patient responses to queries from a bleeding tendency questionnaire. Normal and abnormal class labels were derived from a hematology expert system designed in consultation with a pediatric hematologist. Fuzzy interquartile encoding effected an 11% improvement in the classification accuracy of the underlying neural network classifier with the former data set and 18% with the latter.