EapGAFS: Microarray Dataset for Ensemble Classification for Diseases Prediction

Microarray data stores the measured expression levels of thousands genes simultaneously which helps researchers to get insight into biological and prognostic information. Cancer is a deadly disease that develops over time involves uncontrolled division body cells. In cancer, many are responsible for cell growth division. But different kinds cancer caused by set genes. So be able better understand, diagnose treat it essential know in cells working abnormally. The advances mining, machine learning, soft computing, pattern recognition have addressed challenges posed develop computationally effective models identify new class diagnostic or therapeutic targets. This paper proposed an Ensemble Aprior Gentic Algorithm Feature Selection (EapGAFS) microarray dataset classification. algorithm comprises genetic implemented with aprior learning attributes EapGAFS uses rule mining processing. Through framed model extract attribute features dataset. Finally, ensemble classifier were classified performance conventional classifiers collected breast Hepatities, diabeties, bupa. comparative analysis expressed exhibits improved ~4 – 6% than such as Adaboost ensemble.