An explainable AI approach for diagnosis of COVID-19 using MALDI-ToF mass spectrometry

Abstract

Current artificial intelligence (AI) applications for the diagnosis of coronavirus disease 2019 (COVID-19) often lack a biological foundation in the decision-making process. In this study, we have employed AI for COVID-19 diagnosis using mass spectrometry (MS) data and leveraged explainable AI (X-AI) to explain the decision-making process on a local (per-sample) and global (all samples) basis. We first assessed eight machine learning models with five feature engineering techniques using a five-fold stratified cross-validation. The best accuracy was achieved by Random Forest (RF) classifier using the ratio of areas under the curve (AUC) from the MS data as features. These features were chosen on the basis of tentatively representing both human and viral proteins in human gargle samples. We evaluated the RF classifier on a 70%−30% train-test split strategy of 152 human gargle samples, yielding an accuracy of 94.12% on the test dataset. Employing X-AI, we further interpreted the RF model using shapely additive explanations (SHAP) and feature importance techniques, including permutation and impurity-based feature importances. With these interpretation models offering a local and global explanation for the machine learning model decisions, we devised a straightforward, three-stage X-AI framework that can enable medical practitioners to understand the mechanisms of a black-box AI model. To the medical practitioner, this instills trust in the AI model by providing the rationales for its decisions.