BackgroundKawasaki　disease　(KD)　is　a　leading　cause　of　acquired　heart　disease　in　children　that　is　treated　with　intravenous　immunoglobulin　(IVIG).　However,　10-20%　of　cases　exhibit　IVIG　resistance,　which　increases　the　risk　of　coronary　complications.　Existing　predictive　models　do　not　integrate　multiple　machine　learning　(ML)　algorithms　or　facilitate　real-time　clinical　use.　This　study　presents　a　region-specific,　interpretable　ML　model　for　early　IVIG　resistance　prediction　in　KD.MethodsA　retrospective　cohort　of　463　children　diagnosed　with　KD　at　Fuzhou　University　Affiliated　Provincial　Hospital　(2012-2024)　was　analyzed.　Thirteen　ML　algorithms　were　evaluated　via　cross-validation,　with　performance　assessed　by　AUC　and　other　metrics.　Feature　importance　was　determined　using　SHapley　Additive　exPlanations　(SHAP),　and　risk　of　bias　was　evaluated　using　the　Prediction　Model　Risk　of　Bias　Assessment　Tool.ResultsThe　random　forest　(RF)　model　demonstrated　the　highest　predictive　performance　(AUC　=　0.78).　After　feature　selection　based　on　SHAP　values,　a　final　interpretable　RF　model　incorporating　10　key　features　was　developed,　and　a　web-based　tool　integrating　the　Youden　index　(16.9%)　was　deployed　for　real-time　risk　estimation.ConclusionThis　region-specific,　interpretable　ML　model　(https://milailai.shinyapps.io/data1/)　is　a　practical　tool　for　early　risk　stratification　and　personalized　treatment　of　IVIG　resistance　in　KD.