Parallel and Distributed Population Based Feature Selection Framework for Health Monitoring

Abstract

Smart health monitoring systems have become the subject of an extensive research during the past decades due to their role in improving the quality of health care services. With the increase of heterogeneous data produced by these systems, traditional data preprocessing methods are not able to extract relevant information. Indeed, feature selection is a key phase to preprocess data, it aims to select a relevant feature subset to reach better classification results with an affordable computational cost. In this study, we provide an overview of existing feature selection methods especially those used in the context of Bigdata, pointing out their advantages and drawbacks. Then, we propose a parallel population based feature selection framework for health monitoring.