A lime-based explainable machine learning model for predicting the severity level of covid-19 diagnosed patients

Freddy Gabbay, Shirly Bar-Lev, Ofer Montano, Noam Hadad

Research output: Contribution to journalArticlepeer-review

Abstract

The fast and seemingly uncontrollable spread of the novel coronavirus disease (COVID-19) poses great challenges to an already overloaded health system worldwide. It thus exemplifies an urgent need for fast and effective triage. Such triage can help in the implementation of the necessary measures to prevent patient deterioration and conserve strained hospital resources. We examine two types of machine learning models, a multilayer perceptron artificial neural networks and decision trees, to predict the severity level of illness for patients diagnosed with COVID-19, based on their medical history and laboratory test results. In addition, we combine the machine learning models with a LIME-based explainable model to provide explainability of the model prediction. Our experimental results indicate that the model can achieve up to 80% prediction accuracy for the dataset we used. Finally, we integrate the explainable machine learning models into a mobile application to enable the usage of the proposed models by medical staff worldwide.

Original languageEnglish
Article number10417
JournalApplied Sciences (Switzerland)
Volume11
Issue number21
DOIs
StatePublished - 1 Nov 2021
Externally publishedYes

Keywords

  • COVID-19
  • Deep learning
  • Explainable AI
  • Machine learning

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Instrumentation
  • Fluid Flow and Transfer Processes
  • Process Chemistry and Technology
  • General Materials Science
  • Computer Science Applications

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