Yagin, Fatma HilalCicek, Ipek BalikciAlkhateeb, AbedalrhmanYagin, BurakColak, CemilAzzeh, MohammadAkbulut, Sami2024-08-042024-08-0420230010-48251879-0534https://doi.org/10.1016/j.compbiomed.2023.106619https://hdl.handle.net/11616/101144Aim: COVID-19 has revealed the need for fast and reliable methods to assist clinicians in diagnosing the disease. This article presents a model that applies explainable artificial intelligence (XAI) methods based on machine learning techniques on COVID-19 metagenomic next-generation sequencing (mNGS) samples.Methods: In the data set used in the study, there are 15,979 gene expressions of 234 patients with COVID-19 negative 141 (60.3%) and COVID-19 positive 93 (39.7%). The least absolute shrinkage and selection operator (LASSO) method was applied to select genes associated with COVID-19. Support Vector Machine -Synthetic Minority Oversampling Technique (SVM-SMOTE) method was used to handle the class imbalance problem. Logistics regression (LR), SVM, random forest (RF), and extreme gradient boosting (XGBoost) methods were constructed to predict COVID-19. An explainable approach based on local interpretable model-agnostic expla-nations (LIME) and SHAPley Additive exPlanations (SHAP) methods was applied to determine COVID-19-associated biomarker candidate genes and improve the final model's interpretability.Results: For the diagnosis of COVID-19, the XGBoost (accuracy: 0.930) model outperformed the RF (accuracy: 0.912), SVM (accuracy: 0.877), and LR (accuracy: 0.912) models. As a result of the SHAP, the three most important genes associated with COVID-19 were IFI27, LGR6, and FAM83A. The results of LIME showed that especially the high level of IFI27 gene expression contributed to increasing the probability of positive class.Conclusions: The proposed model (XGBoost) was able to predict COVID-19 successfully. The results show that machine learning combined with LIME and SHAP can explain the biomarker prediction for COVID-19 and provide clinicians with an intuitive understanding and interpretability of the impact of risk factors in the model.eninfo:eu-repo/semantics/openAccessCOVID-19Explainable artificial intelligenceLIMESHAPXGBoostArticle1543673871210.1016/j.compbiomed.2023.1066192-s2.0-85147196638Q1WOS:000931797500001Q1