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Multidimensional Visualization and AI-Driven Prediction Using Clinical and Biochemical Biomarkers in Premature
Kuat Abzaliyev1, Madina Suleimenova2, Symbat Abzaliyeva2
1Department of Internal Medicine, Faculty of Medicine and Healthcare, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.
Insights
This study integrates multidimensional data and AI to identify cardiovascular disease (CVD) risk factors, successfully separating high-risk from low-risk individuals. Findings highlight renal function and hypertension as key predictors for precision prevention.
Area of Science:
- Cardiovascular Research
- Artificial Intelligence in Medicine
- Biomarker Discovery
Background:
- Cardiovascular diseases (CVDs) are the leading cause of global mortality.
- Hypertension, ischemic heart disease (IHD), and cerebrovascular accident (CVA) form a continuum of CVD.
- Existing research often overlooks integrated multidimensional data and AI for pattern discovery.
Purpose of the Study:
- To integrate clinical, biochemical, and lifestyle data for CVD risk profiling.
- To apply multidimensional visualization and AI to identify hidden patterns and risk clusters.
- To develop interpretable models for precision cardiovascular prevention.
Main Methods:
- Analysis of 106 patients with integrated clinical, biochemical (renal function, inflammatory markers, lipids), and lifestyle data.
- Application of correlation analysis, parallel coordinates, t-SNE with k-means clustering, PCA, and Random Forest with SHAP interpretation.
- Bootstrap resampling for confidence intervals of SHAP values to assess feature stability.
Main Results:
- t-SNE clustering achieved complete separation of high-risk (100% CVD-positive) and low-risk (7.8% CVD rate) groups.
- Random Forest identified renal function, hypertension, and physical activity as dominant predictors (Accuracy 0.818, AUC-ROC 0.854).
- SHAP analysis highlighted arterial hypertension, BMI, physical inactivity, renal biomarkers, and NT-proBNP as key predictors.
Conclusions:
- Pioneered integrated multidimensional visualization and AI for interpretable CVD risk profiling.
- Demonstrated the potential for data-driven identification of high- and low-risk clusters.
- Findings suggest potential for interpretable models in precision prevention and decision support for cardiovascular aging.
Abstract:
Background: Cardiovascular diseases (CVDs) remain the primary cause of global mortality, with arterial hypertension, ischemic heart disease (IHD), and cerebrovascular accident (CVA) forming a progressive continuum from early risk factors to severe outcomes. While numerous studies focus on isolated biomarkers, few integrate multidimensional visualization with artificial intelligence to reveal hidden, clinically relevant patterns. Methods: We conducted a comprehensive analysis of 106 patients using an integrated framework that combined clinical, biochemical, and lifestyle data. Parameters included renal function (glomerular filtration rate, cystatin C), inflammatory markers, lipid profile, enzymatic activity, and behavioral factors. After normalization and imputation, we applied correlation analysis, parallel coordinates visualization, t-distributed stochastic neighbor embedding (t-SNE) with k-means clustering, principal component analysis (PCA), and Random Forest modeling with SHAP (SHapley Additive exPlanations) interpretation. Bootstrap resampling was used to estimate 95% confidence intervals for mean absolute SHAP values, assessing feature stability. Results: Consistent patterns across outcomes revealed impaired renal function, reduced physical activity, and high hypertension prevalence in IHD and CVA. t-SNE clustering achieved complete separation of a high-risk group (100% CVD-positive) from a predominantly low-risk group (7.8% CVD rate), demonstrating unsupervised validation of biomarker discriminative power. PCA confirmed multidimensional structure, while Random Forest identified renal function, hypertension status, and physical activity as dominant predictors, achieving robust performance (Accuracy 0.818; AUC-ROC 0.854). SHAP analysis identified arterial hypertension, BMI, and physical inactivity as dominant predictors, complemented by renal biomarkers (GFR, cystatin) and NT-proBNP. Conclusions: This study pioneers the integration of multidimensional visualization and AI-driven analysis for CVD risk profiling, enabling interpretable, data-driven identification of high- and low-risk clusters. Despite the limited single-center cohort (n = 106) and cross-sectional design, the findings highlight the potential of interpretable models for precision prevention and transparent decision support in cardiovascular aging research.
