Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak.
Muhammad NaeemJian YuMuhammad AamirSajjad Ahmad KhanOlayinka AdeleyeZardad KhanPublished in: PeerJ. Computer science (2021)
Statistical measures-Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE)-are used for model accuracy. The values of MAPE for the best-selected models for confirmed, recovered and deaths cases are 0.003, 0.006 and 0.115, respectively, which falls under the category of highly accurate forecasts. In addition, we computed 15 days ahead forecast for the daily deaths, recovered, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting the decision-making of evolving short-term policies.
Keyphrases
- machine learning
- end stage renal disease
- decision making
- ejection fraction
- newly diagnosed
- chronic kidney disease
- public health
- peritoneal dialysis
- artificial intelligence
- high resolution
- big data
- computed tomography
- patient reported outcomes
- magnetic resonance
- dna methylation
- mass spectrometry
- patient reported
- diffusion weighted imaging