Результаты поиска по 'cardiovascular disease':
Найдено статей: 3
  1. Editor’s note
    Computer Research and Modeling, 2024, v. 16, no. 7, pp. 1533-1538
  2. Qaisrani S.N., Khattak A., Zubair Asghar M., Kuleev R., Imbugva G.
    Efficient diagnosis of cardiovascular disease using composite deep learning and explainable AI technique
    Computer Research and Modeling, 2024, v. 16, no. 7, pp. 1651-1666

    During the last several decades, cardiovascular disease has surpassed all others as the leading cause of mortality in both high-income and low-income countries. The mortality rate from heart disorders may be lowered with early identification and close clinical monitoring. However, it is not feasible to adequately monitor patients every day, and 24-hour consultation with a doctor is not a feasible option, since it requires more sagacity, time, and knowledge than is currently available.

    In this study, we examine the Explainable Artificial Intelligence (XAI) technique, namely, the SHAP interpretability approach, in order to educate the medical professionals about the Explainable AI (XAI) methods that can be helpful in healthcare. The XAI methods enhance the trust and understandability of both practitioners and Health Researchers in AI Models. In this work, we propose a composite Deep Learning model: Bi-LSTM+CNN model to effectively predict heart disease from patient data. After balancing the dataset, the Bi-LSTM+CNN model was used. In contrast to other studies, our proposed hybrid deep learning model produced excellent experimental results, including 99.05% accuracy, 99% precision, 99% recall, and 99% F1-score.

    Keywords: explainable AI, cross-validation, backward elimination, REFCV, cardiovascular disease, healthcare.
  3. Usanov D.A., Skripal A.V., Averyanov A.P., Dobdin S.Yu., Kashchavtsev E.O.
    Method of estimation of heart failure during a physical exercise
    Computer Research and Modeling, 2017, v. 9, no. 2, pp. 311-321

    The results of determination of the risk of cardiovascular failure of young athletes and adolescents in stressful physical activity have been demonstrated. The method of screening diagnostics of the risk of developing heart failure has been described. The results of contactless measurement of the form of the pulse wave of the radial artery using semiconductor laser autodyne have been presented. In the measurements used laser diode type RLD-650 specifications: output power of 5 mW, emission wavelength 654 nm. The problem was solved by the reduced form of the reflector movement, which acts as the surface of the skin of the human artery, tested method of assessing the risk of cardiovascular disease during exercise and the analysis of the results of its application to assess the risk of cardiovascular failure reactions of young athletes. As analyzed parameters were selected the following indicators: the steepness of the rise in the systolic portion of the fast and slow phase, the rate of change in the pulse wave catacrota variability of cardio intervals as determined by the time intervals between the peaks of the pulse wave. It analyzed pulse wave form on its first and second derivative with respect to time. The zeros of the first derivative of the pulse wave allow to set aside time in systolic rise. A minimum of the second derivative corresponds to the end of the phase and the beginning of the slow pressure build-up in the systole. Using the first and second derivative of the pulse wave made it possible to separately analyze the pulse wave form phase of rapid and slow pressure increase phase during systolic expansion. It has been established that the presence of anomalies in the form of the pulse wave in combination with vagotonic nervous regulation of the cardiovascular system of a patient is a sign of danger collapse of circulation during physical exercise.

    Keywords: cardiovascular system, pulse wave, diagnostics, semiconductor laser autodyne.
    Views (last year): 8. Citations: 1 (RSCI).

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