The　diagnosis　of　breast　cancer　in　the　middle　and　early　period　is　conducive　to　later　treatment,　but　the　current　diagnosis　rate　is　not　very　desirable.　Using　machine　learning　to　predict　the　benign　and　malignant　of　breast　cancer　can　provide　some　assist　to　doctors＇　treatment　in　clinical　practice.　In　this　paper,　we　have　collected　data　from　digitized　images　of　a　fine　needle　aspirate　(FNA)　of　a　breast　mass.　They　describe　characteristics　of　the　cell　nuclei　presented　in　the　image.　This　work　adopts　several　feature　selection　methods　to　select　the　most　related　features　for　breast　cancer　diagnosis.　Based　on　the　selected　features,　four　machine　learning　models,　Support　Vector　Machine　(SVM),　Decision　Tree　(DT),　AdaBoost　and　Random　Forest　(RF)　are　built　and　their　performance　are　evaluated.　The　experimental　results　show　that　the　accuracy　of　RF　is　higher　than　the　other　three　methods.