Cardiovascular diseases are widespread globally and are one of the leading causes of mortality. This study uses the LSTM (Long Short-Term Memory) deep learning model to predict the impact of weather conditions (temperature, atmospheric pressure, relative humidity, wind speed, and geomagnetic activity) on cardiovascular diseases. The findings demonstrate the model's high accuracy, making it a valuable tool for preventive measures and healthcare. This work lies at the intersection of medicine, meteorology, and artificial intelligence, fostering interdisciplinary collaboration.
| Mualliflar | Pulatov, Giyos, Kabildjanov, Aleksandr, Pulatova, Gulxayo |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2024-12-26 |
| Son | 4 |
| Betlar | 173-177 |
| Til | Rus |
Заболевания сердечно-сосудистой системы, LSTM модель, погодные условия, глубокое обучение, системы прогнозирования, профилактика, система здравоохранения, Yurak-qon bosimi kasalliklari, LSTM modeli, ob-havo sharoitlari, chuqur o‘rganish, bashorat qilish tizimlari, profilaktika, sog‘liqni saqlash tizimi.
This article describes the use of a Multilayer Perceptron (MLP) neural network to study and predict the impact of weather conditions on blood pressure-related diseases. It has been determined that weather elements such…
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