COVID-19 Future Forecasting Using Supervised Machine Learning Models

Machine learning (ML) based forecasting mechanisms have proved their significance to anticipate in perioperative outcomes to improve the decision making on the future course of actions. The ML models have long been used in many application domains which needed the identification and prioritization of adverse factors for a threat. Several prediction methods are being popularly used to handle forecasting problems. This study demonstrates the capability of ML models to forecast the number of upcoming patients affected by COVID-19 which is presently considered as a potential threat to mankind. In particular, four standard forecasting models, such as linear regression (LR), least absolute shrinkage and selection…

• MO
  Ministry of Science, ICT and Future Planning

  Award: NRF-2019R1F1A1060752
• MO
  Ministry of Science and ICT, South Korea