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. 2020 Jul 3:8:357.
doi: 10.3389/fpubh.2020.00357. eCollection 2020.

COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm

Celestine Iwendi  1 , Ali Kashif Bashir  2 , Atharva Peshkar  3 , R Sujatha  4 , Jyotir Moy Chatterjee  5 , Swetha Pasupuleti  6 , Rishita Mishra  7 , Sofia Pillai  6 , Ohyun Jo  8
Affiliations

COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm

Celestine Iwendi et al. Front Public Health. .

Abstract

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.

Keywords: COVID-19; boosting; healthcare analytics; infection; patient data; random forest classification.

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Figures

Figure 1
Figure 1
Symptoms in patients.
Figure 2
Figure 2
Correlation between data features.
Figure 3
Figure 3
Evaluation metrics for decision tree.
Figure 4
Figure 4
Evaluation metrics for SVM classifier.
Figure 5
Figure 5
Evaluation metrics for Gaussian NB.
Figure 6
Figure 6
Evaluation metrics for Boosted Random Forest.
Figure 7
Figure 7
Decision tree.
Figure 8
Figure 8
Decision tree 1.
Figure 9
Figure 9
Decision tree 10.
Figure 10
Figure 10
Decision tree 25.
Figure 11
Figure 11
Decision tree 100.
Figure 12
Figure 12
Comparison of Models' performance.
See this image and copyright information in PMC

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