The　use　of　diameter　stenosis　(DS),　as　revealed　by　coronary　angiography,　for　predicting　fractional　flow　reserve　(FFR)　usually　results　in　a　high　error　rate　of　detection.　In　this　study,　we　investigated　a　method　for　predicting　FFR　in　patients　with　coronary　stenosis　based　on　multiple　independent　risk　factors.　The　aim　of　the　study　was　to　improve　the　accuracy　of　detection.　First,　we　searched　the　existing　literature　to　identify　multiple　independent　risk　factors　and　then　calculated　the　corresponding　odds　ratios.　The　improved　analytic　hierarchy　process　(IAHP)　was　then　used　to　determine　the　weighted　value　of　each　independent　risk　factor,　based　on　the　corresponding　odds　ratio.　Next,　we　developed　a　novel　method,　based　on　the　top　seven　independent　risk　factors　with　the　highest　weighted　values,　to　predict　FFR.　This　model　was　then　used　to　predict　the　FFR　of　253　patients　with　coronary　stenosis,　and　the　results　were　then　compared　with　previous　methods　(DS　alone　and　a　simplified　scoring　system).　In　addition　to　DS,　we　identified　a　range　of　other　independent　risk　factors,　with　the　highest　weighted　values,　for　predicting　FFR,　including　gender,　body　mass　index,　location　of　stenosis,　type　of　coronary　artery　distribution,　left　ventricular　ejection　fraction,　and　left　myocardial　mass.　The　area　under　the　receiver-operating　characteristic　curve　(AUC)　for　the　newly　developed　method　was　84.3%　(95%　CI:　79.2-89.4%),　which　was　larger　than　65.3%　(95%　CI:　61.5-69.1%)　of　DS　alone　and　74.8%　(95%　CI:　68.4-81.2%)　of　the　existing　simplified　scoring　system.　The　newly　developed　method,　based　on　multiple　independent　risk　factors,　effectively　improves　the　prediction　accuracy　for　FFR.