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Owing to the advantages of increased accuracy and the potential to detectunseen patterns, provided by data mining techniques they have been widelyincorporated for standard classification problems. They have often been usedfor high precision disease prediction in the medical field, and several hybridprediction models capable of achieving high accuracies have been proposed.Though this stands true most of the previous models fail to efficiently addressthe recurring issue of bad data quality which plagues most high dimensionaldata, and especially proves troublesome in the highly sensitive medical data.This work proposes a robust self healing (RSH) hybrid prediction model whichfunctions by using the data in its entirety by removing errors andinconsistencies from it rather than discarding any data. Initial processinginvolves data preparation followed by cleansing or scrubbing throughcontext-dependent attribute correction, which ensures that there is nosignificant loss of relevant information before the feature selection andprediction phases. An ensemble of heterogeneous classifiers, subjected to localboosting, is utilized to build the prediction model and genetic algorithm basedwrapper feature selection technique wrapped on the respective classifiers isemployed to select the corresponding optimal set of features, which warranthigher accuracy. The proposed method is compared with some of the existing highperforming models and the results are analyzed.

Robust self-healing prediction model for high dimensional data