COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm
By Celestine Iwendi, Ali Kashif Bashir, Atharva Peshkar, R. Sujatha, Jyotir Moy Chatterjee, Swetha Pasupuleti, R. Mishra, S. Pillai, Ohyun Jo. Frontiers in Public Health, 2020
426 citations recorded by the source corpus.
Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients' gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years.
Subfields: Health Information Management, Modeling and Simulation, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/c4560af30768217dd37877c28448135fd999e318
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Health Information Management
In Radiology, Nuclear Medicine and Imaging
- Thippa Reddy Gadekallu
- Ala Al-Fuqaha
- Quoc-Viet Pham
- Junaid Qadir
- Adeel Razi
- S. Krishnan S
- Sweta Bhattacharya
- Mamoun Alazab
Citations in the corpus
- Remote patient monitoring using artificial intelligence: Current state, applications, and challenges (2023, background): “This can be addressed by adopting balanced accuracy, precision, recall, and F1-score (Iwendi et al., 2020) metrics which provide model performance at each label.”
- Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, positive): “…to combat the COVID-19 pandemic, re-searches have been initiated on scientific studies in all directions, and DL integrated with medical image processing techniques have also been explored rigorously to find a definite solution (Hakak, Khan, Imran, Choo, & Shoaib, 2020; Iwendi et al., 2020).”
- Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, methodology): “In recent work, Iwendi et al. [93] used an RF model with the AdaBoost algorithm to the severity of Covid-19, death, or recovery rate for a patient.”