An interpretable machine learning framework for diagnosis and prognosis of COVID-19.

An interpretable machine learning framework for diagnosis and prognosis of COVID-19.

Publication date: Jul 12, 2023

Coronaviruses have affected the lives of people around the world. Increasingly, studies have indicated that the virus is mutating and becoming more contagious. Hence, the pressing priority is to swiftly and accurately predict patient outcomes. In addition, physicians and patients increasingly need interpretability when building machine models in healthcare. We propose an interpretable machine framework(KISM) that can diagnose and prognose patients based on blood test datasets. First, we use k-nearest neighbors, isolated forests, and SMOTE to pre-process the original blood test datasets. Seven machine learning tools Support Vector Machine, Extra Tree, Random Forest, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, Logistic Regression, and ensemble learning were then used to diagnose and predict COVID-19. In addition, we used SHAP and scikit-learn post-hoc interpretability to report feature importance, allowing healthcare professionals and artificial intelligence models to interact to suggest biomarkers that some doctors may have missed. The 10-fold cross-validation of two public datasets shows that the performance of KISM is better than that of the current state-of-the-art methods. In the diagnostic COVID-19 task, an AUC value of 0. 9869 and an accuracy of 0. 9787 were obtained, and ultimately Leukocytes, platelets, and Proteina C reativa mg/dL were found to be the most indicative biomarkers for the diagnosis of COVID-19. An AUC value of 0. 9949 and an accuracy of 0. 9677 were obtained in the prognostic COVID-19 task and Age, LYMPH, and WBC were found to be the most indicative biomarkers for identifying the severity of the patient.

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Concepts Keywords
Coronaviruses Addition
Forest Biomarkers
Lymph Blood
Professionals Covid
Wbc Datasets
Diagnose
Diagnosis
Healthcare
Interpretability
Interpretable
Learning
Models
Patient
Predict
Test

Semantics

Type Source Name
disease MESH COVID-19
disease IDO blood
disease IDO process
drug DRUGBANK Flunarizine
disease VO report
drug DRUGBANK Tropicamide
drug DRUGBANK Stanolone
disease VO age
drug DRUGBANK Coenzyme M
drug DRUGBANK Cefalotin
disease VO time
disease MESH pneumonia
pathway REACTOME Reproduction
disease MESH critically ill
disease MESH acute respiratory distress syndrome
disease VO organ
disease MESH syndromes
disease MESH MODS
disease MESH infection
disease MESH morbidity
disease IDO intervention
disease IDO algorithm
drug DRUGBANK Ademetionine
drug DRUGBANK Pentaerythritol tetranitrate
disease VO laboratory test
drug DRUGBANK Aspartame
drug DRUGBANK Dextrose unspecified form
drug DRUGBANK Urea
drug DRUGBANK Creatinine
drug DRUGBANK Potassium
drug DRUGBANK L-Alanine
drug DRUGBANK Saquinavir
disease MESH neointima
disease MESH scar
disease VO volume

Original Article

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