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GA BASED FEATURE RANKING MECHANISM TO DETECT NEW BORN INFANTS JAUNDICE WITH AN ENSEMBLE TREE STRATEGY

Jaundice is a yellow discoloration of the skin and whites of the eyes that is often seen in newborns. Newborn jaundice can be analyzed by scrutinizing the infant and examining blood levels of bilirubin. Infants with high blood levels of bilirubin called hyperbilirubinaemia, evolve the yellow color when bilirubin acquires in the skin. The major symptom of jaundice is yellow coloring of the skin and conjunctiva of the eyes. A Genetic Algorithm (GA) is used to enhance or optimize the overall behavior by evolving the population. Genetic Algorithms (GAs) are a virtually new criterion for a search, based on the precept on instinctive selection. Ensemble methods are learning algorithms that design a set of classifiers. An ensemble of classifiers is a predetermined classifier whose individual decisions are combined in some way to classify new examples. In this analysis, an ensemble of fitness evaluations would produce an ensemble of fitness values for each individual. The experimental results reveal that the proposed method can act as a supplement to support earlier detection and more effective treatment due to improved jaundice detection.