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Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed

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This summary is machine-generated.

Machine learning models effectively predict COVID-19 patient mortality using comprehensive data, including CT severity scores. The random forest algorithm demonstrated superior performance, enabling timely risk stratification and improved patient survival.

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Area of Science:

  • Medical Informatics
  • Radiology
  • Computational Biology

Background:

  • Artificial intelligence (AI) and machine learning (ML) are increasingly used for COVID-19 mortality prediction.
  • Existing models primarily use demographics, risk factors, and lab results, with limited focus on imaging data.
  • There's a need for prognostic models incorporating imaging manifestations alongside clinical and laboratory predictors.

Purpose of the Study:

  • To develop an efficient ML prognostic model for COVID-19 mortality prediction.
  • To evaluate the prognostic role of chest CT severity score (CT-SS) in combination with other predictors.
  • To compare the performance of eight different ML algorithms for mortality prediction.

Main Methods:

  • Retrospective review of 55 features from 6854 suspected COVID-19 cases.
  • Chi-square test to identify important predictors for mortality.
  • Training and testing of eight ML algorithms (J48, SVM, MLP, k-NN, NB, LR, RF, XGBoost).
  • Performance evaluation using accuracy, precision, sensitivity, specificity, and AUC.

Main Results:

  • The final sample included 815 COVID-19 positive patients (54.85% male, mean age 57.22 ± 16.76 years).
  • The Random Forest (RF) algorithm achieved the highest performance: 97.2% accuracy, 100% sensitivity, 94.8% precision, 94.5% specificity, and 99.9% AUC.
  • Other ML algorithms showed good prediction performance with AUCs ranging from 81.2% to 93.9%.

Conclusions:

  • ML-based predictive models utilizing routine data, including CT-SS, can accurately stratify COVID-19 patient risk.
  • The proposed RF model demonstrates high efficiency in predicting COVID-19 mortality.
  • This approach facilitates early identification of high-risk patients, optimizes resource allocation, and potentially increases survival rates.