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Prognostication of Covid-19 and Heart Disease: A Combined Approach

机译:对Covid-19和心脏病的预测:一种组合方法

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COVID-19 infection, caused by the virus SARS-Cov 2 is growing at a rapid rate. As an efficient cure has not been available, early detection is integral for disease cure and control. Predictive algorithms are useful in this scenario. Here, estimation is performed on patients who are likely to come in contact with COVID-19 disease, using clinical predictive models with the help of deep learning. The most informative features are extracted from chest X-ray images for COVID-19 patients and non COVID-19 patients. These images are used for COVID detection. Patients with other chronic diseases are more vulnerable to COVID-19. Hence, we put forward a Heart Disease Prediction system based on machine learning algorithms. The feature selection algorithms are utilized in the feature selection procedures for enhancing the classification accuracy and for minimizing the execution time of the classification system.
机译:Covid-19感染,由病毒SARS-COV 2引起的速度快速增长。 由于尚未获得有效的固化,早期检测是疾病治愈和控制的一体化。 预测算法在这种情况下很有用。 在此,对可能在深层学习的帮助下使用临床预测模型接触的患者对患者进行估计。 从Covid-19患者和非Covid-19患者的胸X射线图像中提取最具信息丰富的功能。 这些图像用于Covid检测。 患有其他慢性疾病的患者更容易受Covid-19。 因此,我们提出了一种基于机器学习算法的心脏病预测系统。 特征选择算法用于特征选择过程中,用于提高分类精度并最小化分类系统的执行时间。

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