Author: Satheesh Kumar, R.; Ramakrishnan, S.; Roy, R.; Vijayan, V.; Kavya, K. K. B.
Title: Prognostication of Covid-19 and Heart Disease: A Combined Approach Cord-id: kurutn6z Document date: 2021_1_1
ID: kurutn6z
Snippet: 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
Document: 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. © 2021 IEEE.
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