MULTISINFLAR VA PRETSEDENT OBYEKTLARNI SHAKLLANTIRISH ALGORITMI
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Abstract
Ushbu maqola tibbiy ma’lumotlarni tasniflash samaradorligini oshirish uchun ko’p sinfli va pretsedent obyektlarni yaratish algoritmini taklif qiladi. Ushbu yondashuv diagnostika sinflarining geterogenligini, sonli va nominal xususiyatlarni birgalikda qayta ishlash qobiliyatini hisobga oladi. Har bir boshlang’ich sinf nisbatan bir xil ko’p sinfli sinflarga bo’linadi, ular uchun vakillik pretsedent obyektlari aniqlanadi. Pretsedentlar ularning tipikligi, qamrovi, guruhlararo ajratilishi, ma’lumotlar sifati va klinik validligi asosida tanlanadi. Ko’p sinfli sinflarni yaratish uchun o’xshashlik o’lchovlariga asoslangan k-medoidlar/PAM kabi usullar qo’llaniladi. Axborot xususiyatlari ularning diagnostik ahamiyati va narxiga qarab tanlanadi. Ma’lumotlarning oqib ketishini oldini olish uchun o’qitish va parametrlarni sozlash ichki o’zaro tasdiqlash doirasida amalga oshiriladi. Haqiqiy obyektlardan pretsedent sifatida foydalanish tasniflash natijalarining talqin qilinishini yaxshilaydi. Taklif qilingan algoritm intellektual diagnostika qarorlarini qo’llab-quvvatlash tizimlarini yaratishda qo’llanilishi mumkin.
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