A descriptive study of random forest algorithm for predicting COVID-19 patients outcome.
Jie Wang1, Heping Yu2, Qingquan Hua1
1Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
Peerj
|September 25, 2020
Summary
This study used a random forest algorithm to predict COVID-19 patient mortality. High levels of lactate dehydrogenase (LDH) and myoglobin (Myo) were identified as key predictors of poor prognosis in COVID-19 patients.
Area of Science:
- Medical research
- Clinical diagnostics
- Public health
Background:
- The COVID-19 pandemic poses a significant global health challenge.
- Identifying reliable predictors for COVID-19 patient outcomes is crucial for effective clinical management.
Purpose of the Study:
- To develop a predictive model for COVID-19 patient prognosis using machine learning.
- To identify key clinical indicators for predicting mortality in COVID-19 patients.
Main Methods:
- Collected clinical data from 126 COVID-19 patients.
- Applied a random forest (RF) algorithm for prognosis prediction.
- Utilized SMOTE and RFE for data balancing and feature selection.
Main Results:
- The RF model achieved 100% accuracy in predicting COVID-19 patient prognoses.
- Lactate dehydrogenase (LDH) and Myoglobin (Myo) were identified as optimal predictors.
- Elevated LDH (>500 U/L) and Myo (>80 ng/ml) significantly increased mortality risk.
Conclusions:
- An RF algorithm accurately predicts COVID-19 patient mortality.
- LDH and Myo levels are valuable early indicators for assessing COVID-19 patient prognosis.
- These findings can aid in early risk stratification and clinical decision-making for COVID-19 patients.
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