A Multimodal Machine Learning Approach to Omics-Based Risk Stratification in Coronary Artery Disease
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This study aims at developing a personalized model for coronary artery disease (CAD) risk stratification based on machine learning modelling of non-imaging data, i.e. clinical, molecular, cellular, inflammatory, and omics data. A multimodal architectural approach is proposed whose generalization capability, with respect to CAD stratification, is currently evaluated. Different data fusion techniques are investigated, ranging from early to late integration methods, aiming at designing a predictive model capable of representing genotype-phenotype interactions pertaining to CAD development. An initial evaluation of the discriminative capacity of the feature space with respect to a binary classification problem (No CAD, CAD), although not complete, shows that: (i) kernel-based classification provides more accurate results as compared with neural network-based and decision tree-based modelling, and (ii) appropriate input refinement by feature ranking has the potential to increase the sensitivity of the model.
KeywordsCoronary artery disease Patient risk stratification Multi-modal machine learning
This work is funded by the European Commission: Project SMARTOOL, “Simulation Modeling of coronary ARTery disease: a tool for clinical decision support—SMARTool” GA number: 689068.
Compliance with Ethical Standards
Conflict of Interest: The authors declare that they have no conflict of interest.
Ethical approval: “All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.”
Informed consent: “Informed consent was obtained from all individual participants included in the study.”
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