SUN’IY INTELLEKT ASOSIDA YURAK-QON TOMIR KASALLIKLARI XAVFINI BASHORAT QILISH: GIBRID ENSEMBLE MODEL VA IZOHLANUVCHANLIKKA ASOSLANGAN YONDASHUV
Qabul qilingan: 2026-05-23
Nashr etilgan: 2026-06-06
Annotatsiya
Ushbu maqolada yurak-qon tomir kasalliklari (YQTK) xavfini erta va aniq bashorat qilish maqsadida jadvalli klinik ko’rsatkichlar (XGBoost), elektrokardiografiya vaqt qatorlari (BiLSTM+Attention) va tibbiy tasvirlar (CNN) integratsiyasiga asoslangan gibrid sun’iy intellekt modeli ishlab chiqildi. Model MIMIC-IV, UK Biobank va PTB-XL ma’lumotlar to’plamlari birlashmasida o’qitildi (sinov to’plami n = 10 832), SHAP izohlanuvchanlik tahlili va Federated Learning paradigmasi bilan to’ldirildi. Gibrid model AUC-ROC = 0.968, sezgirlik 92,7% va Brier score = 0,049 ko’rsatkichlariga erishib, an’anaviy Framingham xavf shkalasidan barcha mezonlarda ustun chiqdi (p < 0,001). Olingan natijalar ko’p modellikli, izohlanuvchan va maxfiylikni saqlovchi yondashuvning klinik amaliyotga tatbiq etish istiqbollarini ko’rsatadi.
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