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Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction
Akshaya Karthikeyan1, Akshit Garg1, P K Vinod1
1Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India.
Insights
Machine learning models using routine blood tests can predict COVID-19 mortality risk. Key biomarkers like neutrophils and age achieve 96% accuracy, enabling early and reliable patient treatment strategies.
Area of Science:
- Computational biology
- Medical informatics
- Epidemiology
Background:
- COVID-19 pandemic poses significant challenges to healthcare systems due to high mortality.
- Accurate mortality prediction is crucial for optimizing patient treatment and resource allocation.
- Routine blood tests offer a readily available data source for predictive modeling.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting COVID-19 mortality risk using blood test data.
- To identify key hematological and biochemical biomarkers associated with COVID-19 mortality.
- To assess the early predictive performance of machine learning models for patient outcomes.
Main Methods:
- Trained and compared various machine learning models including neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees.
- Utilized a feature set comprising neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age.
- Evaluated model performance based on accuracy and predictive timing relative to patient outcomes.
Main Results:
- A combination of five features achieved 96% accuracy in predicting mortality.
- The best performing model, XGBoost with neural network classification, predicted outcomes with 90% accuracy up to 16 days prior.
- Key biomarkers identified include neutrophils, lymphocytes, LDH, hs-CRP, and age.
Conclusions:
- Machine learning models utilizing routine blood tests can accurately and early predict COVID-19 mortality.
- The identified biomarkers and predictive models offer practical tools for healthcare decision-making.
- This approach facilitates timely and targeted medical interventions, improving patient management during the pandemic.
Abstract:
The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner.
