High-performance association rule mining: Mortality prediction model for cardiovascular patients with COVID-19 patterns

On a global scale, 213 countries and territories have been affected by the coronavirus outbreak. According to researchers, underlying co-morbidity, which includes conditions like diabetes, hypertension, cancer, cardiovascular disease, and chronic respiratory disease, impacts mortality. The current situation requires for immediate delivery of solutions. The diagnosis should therefore be more accurate. Therefore, it's essential to determine each person's level of risk in order to prioritise testing for those who are subject to greater risk. The COVID-19 pandemic's onset and the cases of COVID-19 patients who have cardiovascular illness require specific handling. The paper focuses on defining the symptom rule for COVID-19 sickness in cardiovascular patients. The patient's chronic condition was taken into account while classifying the symptoms and determining the likelihood of fatality. The study found that a large proportion of people with fever, sore throats, and coughs have a history of stroke, high cholesterol, diabetes, and obesity. Patients with stroke were more likely to experience chest discomfort, hypertension, diabetes, and obesity. Additionally, the strategy scales well for large datasets and the computing time required for the entire rule extraction procedure is faster than the existing state-of-the-art method. © 2023 Faculty of Engineering, Alexandria University

Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

As corona virus disease (COVID-19) is still an ongoing global outbreak, countries around the world continue to take precautions and measures to control the spread of the pandemic. Because of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals, a rapid, reliable, and automatic detection of COVID-19 is in extreme need to curb the number of infections. By analyzing the COVID-19 chest X-ray images, a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm optimizers. The lung region was segmented from the original chest X-ray images and augmented using various transformation operations. Furthermore, the augmented images were fed into the VGG19 deep network for feature extraction. On the other hand, a feature selection method is proposed to select the most significant features that can boost the classification results. Finally, the selected features were input into an optimized neural network for detection. The neural network is optimized using the proposed hybrid optimizer. The experimental results showed that the proposed method achieved 99.88% accuracy, outperforming the existing COVID-19 detection models. In addition, a deep statistical analysis is performed to study the performance and stability of the proposed optimizer. The results confirm the effectiveness and superiority of the proposed approach. © 2022 Tech Science Press. All rights reserved.