Estimation and Conformity Evaluation of Multi-Class Counterfactual Explanations for Chronic Disease Prevention

Recent advances in Artificial Intelligence (AI) in healthcare are driving research into solutions that can provide personalized guidance. For these solutions to be used as clinical decision support tools, the results provided must be interpretable and consistent with medical knowledge. To this end, this study explores the use of explainable AI to characterize the risk of developing cardiovascular disease in patients diagnosed with chronic obstructive pulmonary disease. A dataset of 9613 records from patients diagnosed with chronic obstructive pulmonary disease was classified into three categories of cardiovascular risk (low, moderate, and high), as estimated by the Framingham Risk Score. Counterfactual explanations were generated with two different methods, MUlti Counterfactuals via Halton sampling (MUCH) and Diverse Counterfactual Explanation (DiCE). An error control mechanism is introduced in the preliminary classification phase to reduce classification errors and obtain meaningful and representative explanations. Furthermore, the concept of counterfactual conformity is introduced as a new way to validate single counterfactual explanations in terms of their conformity, based on proximity with respect to the factual observation and plausibility. The results indicate that explanations generated with MUCH are generally more plausible (lower implausibility) and more distinguishable (higher discriminative power) from the original class than those generated with DiCE, whereas DiCE shows better availability, proximity and sparsity. Furthermore, filtering the counterfactual explanations by eliminating the non-conformal ones results in an additional improvement in quality. The results of this study suggest that combining counterfactual explanations generation with conformity evaluation is worth further validation and expert assessment to enable future development of support tools that provide personalized recommendations for reducing individual risk by targeting specific subsets of biomarkers.