In　the　current　era　massive　datasets　in　healthcare　are　becoming　much　more　available　for　analysis,　where　numerous　features　are　designed　or　constructed　to　represent　a　patient.　Feature　selection　algorithms　play　a　key　role　in　reducing　the　data　dimension　thus　speeding　up　the　succeeding　learning　algorithms　as　well　as　improving　predicting　accuracy.　How　to　select　the　appropriate　subset　of　features　with　low　redundancy　is　one　of　the　interesting　problem　in　feature　selection.　In　this　paper,　we　present　a　new　feature　selection　algorithm　which　aims　to　select　low　redundant　features　in　the　setting　of　grouped　variables.　We　adopt　a　global　optimization　method　based　on　Lipschitz　continuity　and　present　evaluation　results　on　several　datasets,　which　demonstrates　the　correctness　and　effectiveness　of　our　algorithm.