An automated feature selection and classification pipeline to improve explainability of clinical prediction models

Abstract

Artificial Intelligence is becoming recently a promising tool to achieve the deployment of personalized medicine in clinical practice. However, healthcare professionals are demanding clinical prediction models with better interpretability of the results in order to achieve an actual adoption and use of these solutions. The eXplainable Artificial Intelligence tackle this issue by offering feature relevance explanations of the model, among other techniques, where the selection of the important features and elimination of the redundant are cornerstones. This work presents a data management pipeline that allows automating the selection of those relevant features as well as the classifier technique that provides the best performance in terms of classification. The pipeline developed, named SCI-XAI (feature Selection and Classification for Improving eXplainable Artificial Intelligence) has been evaluated with 6 clinical datasets in a cross-validation approach as well as in a test set with unseen data. Next, an explainability evaluation has been carried out of the best models obtained by applying the SCI-XAI pipeline. Results obtained show that SCI-XAI achieves the best classification performance by applying different feature selection techniques depending on the variable type of the feature which reduces significantly the features processed by the model. Thus, feature reduction allows increasing the explainability of the models.