An interpretable machine learning approach to estimate the influence of inflammation biomarkers on cardiovascular risk assessment. (March 2023) - Roseiro, M.; Henriques, J.; Paredes, S.; Rocha, T.; Sousa, J.
Highlights: Validation of the importance of inflammation biomarkers in cardiovascular risk assessment. Actually, the obtained results reveal that Albumin, C-Reactive protein and the Leukocyte Count could improve the cardiovascular disease risk assessment provided by the well-known GRACE model. Development of a new method able to provide an interpretable and personalized cardiovascular risk assessment. Increase the trust of physicians through this data driven approach that resembles the clinical reasoning. It incorporates machine learning in a different perspective, assuming personalization and interpretability as central issues. Abstract: Background and objective: Cardiovascular disease has a huge impact on health care services, originating unsustainable costs at clinical, social, and economic levels. In this context, patients' risk stratification tools are central to support clinical decisions contributing to the implementation of effective preventive health care. Although useful, these tools present some limitations, in particular, some lack of performance as well as the impossibility to consider new risk factors potentially important in the prognosis of severe cardiac events. Moreover, the actual use of these tools in the daily practice requires the physicians' trust. The main goal of this work addresses these two issues: (i) evaluate the importance of inflammation biomarkers when combined with a risk assessment tool; (ii) incorporation of personalization and interpretability as key elements of that assessment. Methods: Firstly, machine learning based models were created to assess the potential of the inflammation biomarkers applied in secondary prevention, namely in the prediction of the six month risk of death/myocardial infarction. Then, an approach based on three main phases was created: (i) set of interpretable rules supported by clinical evidence; (ii) selection based on a machine learning classifier able to identify for a given patient the most suitable subset of rules; (iii) an ensemble scheme combining the previous subset of rules in the estimation of the patient cardiovascular risk. All the results were statistically validated ( t -test, Wilcoxon-signed rank test) according to a previous verification of data normality (Shapiro-Wilk). Results: The proposed methodology was applied to a real acute coronary syndrome patients dataset ( N = 1544) from the Cardiology Unit of Coimbra Hospital and Universitary centre. The first assessment was based on the GRACE tool and a Random Forest classifier, the incorporation of inflammation biomarkers achieved SE=0.83; SP=0.84 whereas the original GRACE risk factors reached SE=0.75; SP=0.85. In the second phase, the proposed approach with inflammation biomarkers achieved SE=0.763 and SP=0.778. Conclusions: This approach confirms the potential of combining inflammation markers with the GRACE score, increasing SE and SP, when compared with the original GRACE. Additionally, it assures interpretability and personalization, which are critical issues to allow its application in the daily clinical practice.