Deep Learning Algorithms for Forecasting COVID-19 Cases in Saudi Arabia

In the recent past, COVID-19 epidemic has impeded global economic progress and, by extension, all of society. This type pandemic spread rapidly, posing a threat to human lives and economy. Because growing scale cases, employing artificial intelligence for future prediction purposes during this is crucial. Consequently, major objective research paper compare various deep learning forecasting algorithms, including auto-regressive integrated moving average, long short-term memory, conventional neural network techniques forecast how would in Saudi Arabia terms number people infected, deaths, recovered cases. Three different time horizons were used predictions: forecasting, medium-term long-term forecasting. Data pre-processing feature extraction steps performed as an integral part analysis work. Six performance measures applied comparing efficacy developed models. LSTM CNN algorithms have shown superior predictive precision with errors less than 5% measured on available real data sets. The best model predict confirmed death cases LSTM, which better RMSE R2 values. Still, similar comparative LSTM. unexpectedly badly when predicting values 641.3 0.313, respectively. work helps decisionmakers health authorities reasonably evaluate status country act accordingly.