Қазақ ұлттық қыздар педагогикалық университеті / Казахский национальный женский педагогический университет

Постоянный URI для этого сообществаhttps://cris.qyzpu.edu.kz/handle/123456789/1

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2024 - 20252

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Тема: search.filters.subject.early warning systems

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    AN EARLY WARNING SYSTEM FOR HOSPITALS USING A WEARABLE ECG DEVICE AND MACHINE LEARNING
    (Mukhametzhan Tynyshbayev ALT University, 2024-09-26)
    Zhadyra N. Alimbayeva
    ;
    Chingiz A. Alimbayev
    ;
    Dilfuza Akhmedova
    ;
    Кassymbek Ozhikenov
    ;
    Zhansulu Ualieva
    According to the World Health Organization, many clinical errors in medical practice occur due to incomplete or untimely access to patient information. The introduction of nurse call systems significantly improves communication between medical staff and patients, increases response efficiency and patient satisfaction. Traditional nurse call systems based on patients' manual requests for medical care are insufficient to prevent unforeseen medical situations. This article proposes a new approach based on the wearable ECG devices developed by the authors for monitoring the cardiovascular system of patients. This approach uses machine learning techniques to analyze data from wearable ECG devices, which allows timely detection of dangerous patient conditions and necessary medical interventions. The proposed early warning system for hospitals performs various important functions, such as continuous recording of the electrical activity of the heart, preprocessing data to reduce noise and improve accuracy, extracting key parameters for further analysis, forming diagnostic indicators and a comprehensive assessment of the condition of the heart to identify potential anomalies.
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    Flood forecasting using machine learning methods in a visual programming environment
    (2025-07-17)
    Gulzat Z. Ziyatbekova
    ;
    Dauren Darkenbayev
    Floods are among the most destructive natural phenomena, significantly impacting the environment, economy, infrastructure, and public safety. Effective forecasting of such emergencies is crucial, especially in the context of global climate change and the increasing population density in coastal and low-lying areas. This work aims to develop and analyze flood forecasting models using machine learning algorithms within the Orange visual software environment. The study employed five algorithms: Random Forest, Decision Tree, Gradient Boosting, AdaBoost, and K-Nearest Neighbors. A comparative analysis of these models was conducted using key classification metrics, including Accuracy, Precision, Recall, and AUC. Special emphasis was placed on visualizing the results and assessing the usability of models within the Orange environment. The findings can be valuable for educational purposes, such as teaching students to work with real environmental data, as well as for practical applications in early warning systems and flood monitoring.