KARDIOSKLEROZ PROGNOSTIK MODELINI YARATISHDA MACHINE LEARNING VA DEEP LEARNING USULLARINING QIYOSIY TAHLILI
##semicolon##
Kardioskleroz, machine learning, deep learning, gibrid model, Random Forest, SVM, CNN, prognostik model, tasviriy ma’lumotlar, ma’lumotlarni qayta ishlash.##article.abstract##
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.
##submission.citations##
1.Chen J., Smith K.L., Li X. Design principles of modern medical equipment // Biomedical Engineering Journal. - 2023. - Vol. 70(4). - P. 1125-1134.
2.Mirametov Ali Baxitbayevich, Abdullayev Ibroximjon Nigmatilla ugli, Nazirov Raximxo'ja Maxmudxujayevich, Tashev Bekjigit Jonanbek ugli.APPLICATION OF ARTIFICIAL INTELLIGENCE IN ECG ANALYSIS: PROBLEMS AND THEIR SOLUTIONS IN HEALTHCARE.SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 3 ISSUE 3 MARCH 2024.110-115.
3.Nematov SQ, Kamolova YM, Abdullayev IN. Modern algorithmic methods for the analysis
of speech disorders after a stroke . Science and Education. 2023;4(6):452-456.
4.Abdullaev I. N.,Magrupov T. M., Nazirov R. M.FORMATION OF A DATABASE OF LUNG DISEASE SOUND SIGNALS. SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 3 ISSUE 9 SEPTEMBER 2024 ISSN: 2181-3337 | SCIENTISTS.UZ. 90-96.
5.Kumar R., Patel D.R., Wang Y. Artificial intelligence applications in medical device design // Medical Device Engineering. - 2022. - Vol. 45(3). - P. 234-245.
