Automatic Extraction of Interpretable Knowledge to Predict the Survival of Patients with Heart Failure

Cardiovascular diseases cause the death of almost 18 million people each year. Heart failure takes place when the heart does not pump a sufficient amount of blood to the body and is one of the most common causes of death. Healthcare technologies and Artificial Intelligence algorithms constitute promising solutions useful not only for the monitoring of people's health but also in order to tell which subjects are more prone to this problem, which is information of paramount relevance to save their lives. The goal of this paper is to understand the predictability of mortality of subjects suffering from left ventricular systolic dysfunction who previously experienced heart failures. To perform this important study, a publicly-available data set is considered that contains thirteen pieces of clinical, body, and lifestyle information about 299 subjects. In tackling this data set, not only do we wish to perform classification with reference to subjects' survival/death, but we also wish to automatically extract explainable knowledge about the reasons for the classification proposed. To this aim, we use DEREx, an Artificial Intelligence-based tool that relies on Evolutionary Algorithms and provides users with an easy-to-understand set of IF-THEN rules containing data set parameters. In this way, it performs the selection of the parameters that are the most relevant for the purpose of classification. We have run our experiments following a sound protocol established in the scientific literature for this data set. Our findings show that, apart from automatically obtaining easily interpretable knowledge, DEREx achieves better results in terms of widely-used quality indices as Matthews Correlation Coefficient, accuracy, and F score.