COVID-19 Patient Health Prediction using Artificial Intelligence Boosted Random Forest Algorithm

Abdul  Subhan; Tuba  Rasheed; Zarwa  Shah; Sadia  Noor; Muhammad Aamir  Khan; Usman  Shakoor

doi:10.48165/sajssh.2021.2313

Authors

Abdul Subhan Masters Scholar at Economics Department COMSATS University Islamabad.
Tuba Rasheed Masters Scholar at Economics Department COMSATS University Islamabad.
Zarwa Shah Masters Scholar at Economics Department COMSATS University Islamabad.
Sadia Noor Masters Scholar at Economics Department COMSATS University Islamabad.
Muhammad Aamir Khan Assistant Professor at Economics Department COMSATS University Islamabad.
Usman Shakoor Assistant Professor at Economics Department COMSATS University Islamabad.

DOI:

https://doi.org/10.48165/sajssh.2021.2313

Keywords:

COVID-19, Predictions, Boosted Random Forest Algorithm, Pakistan

Abstract

In the current times, there is high demand for artificial intelligence (AI) techniques to be integration with real-time collection, wireless infrastructure, as well as processing in terms of end-user devices. It is now remarkable to make use of AI for detection as well as prediction of pandemics that are extremely large in nature. Coronavirus pandemic of 2019 (COVID-19) began in Wuhan, China and caused the deaths of 175,694 deaths around the world, while the number of active patients stands at 254,4792 patients around the world. In Pakistan, from January 2020 March 2021, there have been 658,132 positive cases, 603,512 recovered cases of COVID-19 with 16,208 deaths, reported by world health organization. Nonetheless, the quick and exponential increase in COVID-19 patients has made it necessary that quick and efficient predictions be made in terms of the possible outcomes with respect to the patient for the sake of suitable treatment by making use of AI techniques. A fine-tuned random forest model has been proposed by this paper, which has been given a boost by AdaBoost algorithm. The COVID-19 patient’s health, geographical area, gender, and marital status are used for the prediction of severity in terms of cases as well as possible outcomes, either recovery or no recovery (i.e. death). The model is 90% accurate and has a 0.76 F1 Score on the set of data used. Analysis of data shows a positive correlation with respect to the gender of patient, and death. It also shows that most of the patients had ages between twenty years and seventy years.

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