OB-HAVO OMILLARINING YURAK-QON TOMIR KASALLIKLARIGA TA’SIRINING SUN’IY INTELLEKT ASOSIDA TAHLILI
Keywords:
Yurak-qon tomir kasalliklari, ob-havo omillari, sun'iy intellekt, chuqur o'qitish, MLP, GRU, LSTM, ANFIS, bashoratlash, geomagnit faollik, iqlim o'zgarishlari, ishemiya, aritmiya, gipertoniya.Abstract
This article studies the issues of analyzing the impact of weather factors on
cardiovascular diseases based on artificial intelligence. The complex impact of weather factors
such as temperature, pressure, humidity, wind speed and geomagnetic storms on cardiovascular
diseases such as ischemia, arrhythmia and hypertension is studied. An approach based on artificial
intelligence technologies MLP, GRU, LSTM and ANFIS models is proposed. The results of the study
showed that artificial intelligence models work with high accuracy in predicting the sensitivity of
the cardiovascular system to weather factors and the risk of disease.
References
Sabitovich K.A., Pulatov G., Qizi P.G.A. Ob-havo sharoitlarining yurak-qon tomir kasalliklariga ta’sirini aniqlashning analitik tahlili // Al-Farg‘oniy avlodlari. – 2024. – № 2. – B. 296–300.
Кабилджанов А., Пулатов Г., Пулатова Г. Bashoratlash usul va algoritmlari // Информатика и инженерные технологии. – 2023. – Т. 1, № 2. – B. 124–126.
Zhang X. et al. Time Series Analysis of the Impact of Meteorological Conditions and Air Quality on the Number of Medical Visits for Hypertension in Haikou City, China // Frontiers in Public Health. – 2024.
Ohashi Y. et al. Machine Learning Analysis and Future Risk Prediction of Weather-Sensitive Cardiovascular Disease Mortality During Summer in Tokyo, Japan // Scientific Reports. – 2023.
Helsper M. et al. The Subarachnoid Hemorrhage–Weather Myth: A Long-Term Big Data and Deep Learning Analysis // Frontiers in Neurology. – 2021.
Zirbo S.G.V. et al. Predicting Health Outcomes Using Weather Data: A Dual Machine Learning Approach // Procedia Computer Science. – 2024.
Hsiao H.C.W. et al. Deep Learning for Risk Analysis of Specific Cardiovascular Diseases Using Environmental Data and Outpatient Records // 2016.