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An interpretable machine learning approach to estimate the influence of inflammation biomarkers on cardiovascular
M Roseiro1, J Henriques1, S Paredes2
1CISUC, Center for Informatics and Systems of University of Coimbra, Coimbra 3030-290, Portugal.
Insights
This study enhances cardiovascular risk assessment by integrating inflammation biomarkers with the GRACE score, improving prediction accuracy and offering personalized, interpretable results for better clinical decision-making in acute coronary syndrome patients.
Area of Science:
- Cardiology
- Biomarkers
- Machine Learning
Background:
- Cardiovascular disease poses significant healthcare costs and necessitates effective risk stratification tools.
- Current risk assessment tools have limitations in performance and incorporating novel risk factors.
- Physician trust and adoption are crucial for the daily clinical use of risk assessment tools.
Purpose of the Study:
- To evaluate the impact of inflammation biomarkers when combined with existing risk assessment tools.
- To develop a personalized and interpretable cardiovascular risk assessment approach.
- To improve the accuracy and clinical utility of risk stratification for severe cardiac events.
Main Methods:
- Machine learning models were developed to assess inflammation biomarkers for predicting 6-month mortality/myocardial infarction risk.
- An interpretable rule-based system was created, with a machine learning classifier selecting relevant rules for personalization.
- An ensemble scheme combined selected rules to estimate patient cardiovascular risk, with statistical validation.
Main Results:
- Combining inflammation biomarkers with the GRACE score and Random Forest improved sensitivity (SE=0.83) and specificity (SP=0.84) compared to the original GRACE score (SE=0.75, SP=0.85).
- The personalized approach incorporating inflammation biomarkers achieved SE=0.763 and SP=0.778.
- The methodology was validated on a dataset of 1544 acute coronary syndrome patients.
Conclusions:
- Integrating inflammation biomarkers with the GRACE score enhances predictive accuracy (SE and SP).
- The proposed approach offers personalization and interpretability, crucial for clinical practice adoption.
- This method supports better clinical decisions and preventive healthcare strategies for cardiovascular disease.
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.
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