COVID-19 Perspective Diagnosis of Arrhythmia: A Machine Learning-Based Approach.

On the one hand, we live in an era where many chronic diseases are taking away the lives of humans on this planet. On the other hand, the availability of unprecedented clinical data emanating from electronic health records, wearable sensors, biomedical imaging, and multi-omics draws the attention of Machine Learning Based Experts to adopt techniques to arrive at valuable, actionable insights into the disease diagnosis process. In recent days, one such pandemic that has outburst and taken away the lives of millions of people across the globe is Corona Virus 2019 (COVID-19). Cardiac Arrhythmia was the baseline comorbidity and is an incident with COVID-19. The main aim of our study is to adopt machine learning-based analytical methods to classify the UCI Arrhythmia data set, which can enhance the prediction accuracy of diagnosis of Cardiac Arrhythmia. The Study with the motive of Prevention is Better than Cure mainly acts as a prognosis mechanism for the early detection of COVID-19 via predicted results obtained through Arrhythmia prediction, acting as inputs to alarm the patients with the probability of being affected with COVID-19. The simulation results show that Cat Boost and k-NN have performed well compared to other classifiers. In Feature Selection using ANOVA, Cat Boost outperformed as the best classifier comparatively. In Feature Selection using the Genetic algorithm (using TPOT), XG Boost (XGB) classifier exhibited the best performance. SVM is the best classifier in the Feature Reduction technique using the Genetic algorithm.