PREDICTION AND EVALUATION OF THE COVID-19 PANDEMIC USING LR, RF, SVM, LSTM

As a result of the COVID-19 pandemic, global healthcare systems have been pushed to their limits, making it clear that intelligent support for swift diagnosis and better management is urgently needed. It looks into how SVM, RF, KNN and ANN machine learning algorithms can be used to understand and estimate the influence of COVID-19. The models were trained using their laboratory test results. All algorithms were tested using accuracy, precision, recall and F1-score. The results from experiments suggest that the ANN model reached the highest accuracy of 95.6%, exceeding RF, SVM and KNN at 93.1%, 90.4% and 88.7%, respectively. Its high recall and F1-score (96.2% and 95.8%, respectively) illustrate that ANN works well in detecting challenging features found in medical data. This study included descriptions of the algorithms and tables to explain and reproduce t results more clearly.The results will likely enhance methods used to react to future outbreaks.