QANDLI DIABET KASALLIGI NAZORATI VA PROGNOZI UCHUN NEYRON TARMOQLAR ASOSIDA IOT MA’LUMOTLARINI INTELLEKTUAL QAYTA ISHLASH ALGORITMLARI
Kalit so'zlar
https://doi.org/10.47390/ts-v3i7y2025N2Kalit so'zlar
IoT, qandli diabet, neyron tarmoqlar ,ma’lumotlarni intellektual qayta ishlash , monitoring va prognozlash, sun’iy intellekt.Annotasiya
Qandli diabet tez tarqaluvchi surunkali kasallik bo‘lib, IoT sensorlar yordamida glyukoza, yurak urishi va boshqa fiziologik parametrlarni real vaqt rejimida monitoring qilish mumkin. Ushbu tadqiqotda CNN, RNN va LSTM modellarining samaradorligi solishtirilib, LSTM eng yuqori aniqlikni (92.1%) ko‘rsatdi. Takroriy yozuvlarni bartaraf etish model samaradorligini oshirdi.
Manbalar
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2. Valsalan, P., & Kumar, S. (2022). IoT based expert system for diabetes diagnosis and management. *Journal of Healthcare Engineering*, 2022, 1–10.
3. Ahmed, A., et al. (2023). Performance of AI models in estimating blood glucose level using non-invasive wearable devices. *Computers in Biology and Medicine*, 165, 105234.
4. Mansour, M., & Al-Kahtani, M. (2024). Wearable devices for glucose monitoring: A review. *Sensors*, 24(3), 1–20.
5. Liu, Y., Zhang, L., & Li, J. (2025). Advanced applications in chronic disease monitoring using IoT devices. *Frontiers in Public Health*, 13, 1510456.()
6. Shukurillayev, K. S. (2023). Sun’iy neyron tarmoqlarga asoslangan tibbiy diagnostikaning asosiy bosqichlari. Zenodo. [https://zenodo.org/record/7886536] (https://zenodo.org/record/7886536).
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