We　introduce　a　method　for　modeling　cancer　diagnosis　at　the　molecular　level　using　a　Chinese　microarray　gastric　cancer　dataset.　The　method　combines　an　artificial　neural　network　with　a　decision　tree　that　is　intended　to　precede　standard　techniques,　such　as　classification,　and　enhance　their　performance　and　ability　to　detect　cancer　genes.　First,　we　used　the　relief　algorithm　to　select　the　featured　genes　that　could　unravel　cancer　characteristics　out　of　high　dimensional　data.　Then,　an　artificial　neural　network　was　employed　to　find　the　biomarker　subsets　with　the　best　classification　performance　for　distinguishing　cancerous　tissues　and　their　counterparts.　Next　a　decision　tree　expression　was　used　to　extract　rules　subsets　from　these　biomarker　sets.　Rules　induced　from　the　best　performance　decision　tree,　in　which　the　branches　denote　the　level　of　gene　expression,　were　interpreted　as　a　diagnostic　model　by　using　previous　biological　knowledge.　Finally,　we　obtained　a　gastric　cancer　diagnosis　model　for　Chinese　patients.　The　results　show　that　using　the　Chinese　gastric　biomarker　genes　with　the　diagnostic　model　provides　more　instruction　in　biological　experiments　and　clinical　diagnosis　reference　than　previous　methods.