Stacking Ensemble-Based Intelligent Machine Learning Model for Predicting Post-COVID-19 Complications

The recent outbreak of novel coronavirus disease (COVID-19) has resulted in healthcare crises across the globe. Moreover, the persistent and prolonged complications of post-COVID-19 or long COVID are also putting extreme pressure on hospital authorities due to the constrained healthcare resources. Out of many long-lasting post-COVID-19 complications, heart disease has been realized as the most common among COVID-19 survivors. The motivation behind this research is the limited availability of the post-COVID-19 dataset. In the current research, data related to post-COVID complications are collected by personally contacting the previously infected COVID-19 patients. The dataset is preprocessed to deal with missing values followed by oversampling to generate numerous instances, and model training. A binary classifier based on a stacking ensemble is modeled with deep neural networks for the prediction of heart diseases, post-COVID-19 infection. The proposed model is validated against other baseline techniques, such as decision trees, random forest, support vector machines, and artificial neural networks. Results show that the proposed technique outperforms other baseline techniques and achieves the highest accuracy of 93.23%. Moreover, the results of specificity (95.74%), precision (95.24%), and recall (92.05%) also prove the utility of the adopted approach in comparison to other techniques for the prediction of heart diseases.