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. 2024 Jan 2;14:1. doi: 10.1038/s41598-023-50600-8

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Methodology and study design, workflow, and bioinformatics. This figure presents implemented statistical tests (Recursive Feature Elimination, Pearson correlation, Chi-square test, and Analysis of Variance) for the exploratory data analysis to assess the differences in genomics and phenotypic features between healthy individuals and patients with CVD and observe significant biomarkers. Next, applied a nexus of Machine Learning (ML) algorithms (Random Forest, Support Vector Machine, Xtreme Gradient Boosting Decision Trees, and k-Nearest Neighbors) to predict CVD. In addition, it includes Training Dataset, Test Dataset, Soft Voting Classifier, and Visualization of Type I and II errors.