Key result
Entropy-based AI clustering identifies 10 extreme-heterogeneity attributes and reveals distinct cardiovascular phenotypes.
Why the study?
Conventional approaches often fail to capture the inherent heterogeneity and complexity of cardiovascular disease risk profiles, motivating the use of entropy-based measures and AI/ML-driven clustering to quantify risk factor complexity and identify latent disease patterns.
Cross-Sectional
Combining entropy-based analysis with unsupervised machine learning clustering provides a framework to quantify cardiovascular disease heterogeneity and identify distinct clinical phenotypes.
Answers are grounded in this paper and cite their sources.
May delineate cardiovascular heterogeneity via entropy clustering; hypothesis-generating and leaves open prospective validation before clinical use.
Cardiovascular Diseases (CVDs) represent a leading cause of global morbidity and mortality. Conventional approaches often fail to capture the inherent heterogeneity and complexity of CVDs risk profiles. Therefore, this study aims to quantify complexity among cardiovascular risk factors using entropy-based measures and to identify latent disease patterns through artificial intelligence (AI)/Machine Learning (ML)-driven clustering. To achieve this objective a cross-sectional primary dataset of confirmed CVDs cases was analyzed using Shannon entropy, normalized entropy, joint entropy, and principal component analysis (PCA). Unsupervised AI/ML techniques such as, Variance Threshold approach, Silhouette score, K-means clustering and Cluster-wise descriptive statistics and entropy values were computed to identify phenotypic subgroups. Furthermore, the results were plotted using various graphical techniques. Statistical packages/softwares Python and R were used for the purpose of analysis. The findings revealed that the ten attributes with highest and lowest entropy values are displayed, indicating substantial heterogeneity. Moreover, the elevated joint entropy reflects strong interactions and co-variability, underscoring the multidimensional nature of cardiovascular disease profiles within the study cohort. Correspondingly ML/AI based clustering identified distinct cardiovascular phenotypes with varying demographic and clinical characteristics. Overall, the Entropy-based analysis combined with AI techniques provides a robust framework for understanding cardiovascular disease complexity and may assist in improved population level assessment and targeted interventions.
No takes yet. Share an insight, caveat, or question.
Saini et al. (2026) conducted a cross-sectional in Cardiovascular Diseases (CVDs). Entropy-based measures and unsupervised AI/ML-driven clustering (Shannon entropy, normalized entropy, joint entropy, PCA, Variance Threshold, Silhouette score, K-means clustering) was evaluated on Quantification of cardiovascular risk factor complexity/heterogeneity and identification of latent phenotypic subgroups. Entropy-based analysis and AI/ML clustering identified 10 attributes with extreme entropy values and distinct cardiovascular phenotypes with varying clinical characteristics.
Synapse has enriched 10 related papers. Consider them for comparative context: