Mortality Prediction in COVID-19 Using Time Series and Machine Learning Techniques
Predicting mortality in COVID-19 is one of the most significant and difficult tasks at hand. This study compares time series and machine learning methods, including support vector machines (SVMs) and neural networks (NNs), to forecast the mortality rate in seven countries: the United States, India,...
Saved in:
Main Authors: | Tanzina Akter, Md. Farhad Hossain, Mohammad Safi Ullah, Rabeya Akter |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2024-01-01
|
Series: | Computational and Mathematical Methods |
Online Access: | http://dx.doi.org/10.1155/2024/5891177 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Comparative Study between Time Series and Machine Learning Technique to Predict Dengue Fever in Dhaka City
by: Tanzina Akter, et al.
Published: (2024-01-01) -
First-principles investigation of half-metallic CaTGe2O6 (T = Mn, Fe, Co) clinopyroxenes: Potential for spintronics and optoelectronics applications
by: Tasmi Akter, et al.
Published: (2025-01-01) -
Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources
by: Jared D. Willard, et al.
Published: (2025-01-01) -
Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach
by: Iqramul Haq, et al.
Published: (2022-01-01) -
Revisiting Agamben’s Homo Sacer: Sovereign Power and Bare Life – A Critical Review
by: Md. Lab Hossain, et al.
Published: (2025-01-01)