KARDIOSKLEROZ PROGNOSTIK MODELINI YARATISHDA MACHINE LEARNING VA DEEP LEARNING USULLARINING QIYOSIY TAHLILI
Ключевые слова:
Kardioskleroz, machine learning, deep learning, gibrid model, Random Forest, SVM, CNN, prognostik model, tasviriy ma’lumotlar, ma’lumotlarni qayta ishlash.Аннотация
Ushbu tadqiqotda kardioskleroz kasalligini prognoz qilish uchun machine learning va deep learning usullari asosida turli modellar tahlil qilindi. Random Forest, SVM va CNN modellarining samaradorligi baholandi va ularning kombinatsiyasidan foydalangan gibrid yondashuv eng yuqori aniqlikka erishdi (91.2%). Tadqiqot davomida ma’lumotlarni qayta ishlash jarayonlari amalga oshirilib, tasviriy va raqamli ma’lumotlarga mos pre-processing ishlari bajarildi. Natijalar shuni ko‘rsatdiki, modellar kombinatsiyasi prognostik aniqlikni 5-7% ga oshiradi, bu esa klinik amaliyot uchun samarali echim bo‘lishi mumkin.
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