Growing　interest　in　genomics　research　has　called　for　new　semiparametric　models　based　on　kernel　machine　regression　for　modeling　health　outcomes.　Models　containing　redundant　predictors　often　show　unsatisfactory　prediction　performance.　Thus,　our　task　is　to　construct　a　method　which　can　guarantee　the　estimation　accuracy　by　removing　redundant　variables.　Specifically,　in　this　paper,　based　on　the　regularization　method　and　an　innovative　class　of　garrotized　kernel　functions,　we　propose　a　novel　penalized　kernel　machine　method　for　a　semiparametric　logistic　model.　Our　method　can　promise　us　high　prediction　accuracies,　due　to　its　capability　of　flexibly　describing　the　complicated　relationship　between　responses　and　predictors　and　its　compatibility　of　the　interactions　among　the　predictors.　In　addition,　our　method　can　also　remove　the　redundant　variables.　Our　numerical　experiments　demonstrate　that　our　method　yields　higher　prediction　accuracies　compared　to　competing　approaches.