Enhancing Framingham Cardiovascular Risk Score Transparency through Logic-Based XAI
2026-02-25 • Logic in Computer Science
Logic in Computer ScienceArtificial Intelligence
AI summaryⓘ
The authors explain that while the Framingham Risk Score (FRS) helps predict heart disease risk, it doesn't show why a person is at a certain risk or how to lower it. They created a new tool based on logic and explainable AI that identifies the key health factors behind each risk score. Their tool also suggests specific changes a person could make to reduce their risk. Testing on many examples showed the tool works well and could help doctors better understand and use risk scores, especially where specialists are scarce.
Cardiovascular diseaseFramingham Risk ScoreRisk predictionExplainable AIFirst-order logicClinical decision supportRisk factorsModifiable variablesTransparency in AIHealth interventions
Authors
Emannuel L. de A. Bezerra, Luiz H. T. Viana, Vinícius P. Chagas, Diogo E. Rolim, Thiago Alves Rocha, Carlos H. L. Cavalcante
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.