Enhancing Framingham Cardiovascular Risk Score Transparency through Logic-Based XAI

TL;DR

This paper introduces a logic-based explainable AI tool that enhances the transparency of the Framingham Risk Score for cardiovascular disease, helping clinicians understand and act on risk factors more effectively.

Contribution

It presents a novel logical explainer that identifies key patient attributes and actionable scenarios to clarify the FRS's risk assessments.

Findings

Successfully identified important risk factors for over 22,000 cases

Generated actionable interventions to reduce risk categories

Improved transparency and trust in CVD risk prediction

Abstract

Cardiovascular disease (CVD) remains one of the leading global health challenges, accounting for more than 19 million deaths worldwide. To address this, several tools that aim to predict CVD risk and support clinical decision making have been developed. In particular, the Framingham Risk Score (FRS) is one of the most widely used and recommended worldwide. However, it does not explain why a patient was assigned to a particular risk category nor how it can be reduced. Due to this lack of transparency, we present a logical explainer for the FRS. Based on first-order logic and explainable artificial intelligence (XAI) fundaments, the explainer is capable of identifying a minimal set of patient attributes that are sufficient to explain a given risk classification. Our explainer also produces actionable scenarios that illustrate which modifiable variables would reduce a patient's risk category. We evaluated all possible input combinations of the FRS (over 22,000 samples) and tested them with our explainer, successfully identifying important risk factors and suggesting focused interventions for each case. The results may improve clinician trust and facilitate a wider implementation of CVD risk assessment by converting opaque scores into transparent and prescriptive insights, particularly in areas with restricted access to specialists.