Title : Risk prediction and quantification of cardiovascular disease using machine learning: Advancing early diagnosis with clinical insights
Abstract:
Cardiovascular diseases (CVDs) account for nearly 18 million deaths annually, placing a significant burden on global healthcare systems, particularly in underrepresented regions. Early risk prediction remains critical for timely interventions and improved patient outcomes. This study employs advanced machine learning (ML) techniques to develop a robust framework for cardiovascular risk stratification and prediction. By integrating the novel KMC Heart dataset, which reflects region-specific risk factors and demographic diversity, with the widely recognized UCI Heart Disease dataset, this research bridges key gaps in existing predictive models, enhancing their generalizability and clinical relevance. The study is structured into two key phases: risk clustering and predictive modeling. In Part I, unsupervised clustering techniques, including K-means and DBSCAN, stratify patients into clinically meaningful risk groups. K-means identifies three distinct clusters (low, moderate, and high risk), while DBSCAN detects extreme-risk outliers patients with systolic blood pressure (SBP) > 180 mmHg and fasting glucose > 200 mg/dL often missed in traditional models. This dual clustering approach offers nuanced insights, refining cardiovascular risk classification beyond conventional clinical thresholds.
In Part II, supervised ML models Random Forest, Support Vector Machines (SVM), and Decision Trees are trained and validated using a harmonized feature set that includes age, SBP, cholesterol, fasting glucose, resting ECG, and exercise-induced angina. Rigorous preprocessing, including Z-score normalization, feature selection based on clinical relevance, and hyperparameter optimization, ensures model robustness. Random Forest emerges as the top-performing algorithm, achieving an AUC-ROC of 0.89 on the Kasturba dataset and 0.85 on the UCI dataset, outperforming existing benchmarks. This performance highlights Random Forest's ability to balance precision (0.85) and recall (0.82), making it ideal for real-world deployment. Key findings include the synergistic integration of the Kasturba dataset, which improves model accuracy by 12%, addressing demographic homogeneity limitations of the UCI dataset. The identified risk clusters align with established clinical patterns while offering additional granularity for borderline and extreme-risk cases. These insights facilitate early identification of high-risk individuals, enabling targeted interventions and optimized resource allocation.
The study underscores the clinical applicability of ML models in cardiovascular care. Predictive models, particularly Random Forest, can be seamlessly integrated into electronic health record (EHR) systems to automate risk assessment, flagging high-risk patients for early intervention. Additionally, the clustering framework complements traditional risk scoring tools, identifying subgroups that benefit from personalized prevention strategies. Future directions include enhancing datasets with longitudinal data, incorporating emerging biomarkers (e.g., genomic and inflammatory markers), and exploring deep learning methods such as CNNs for imaging data. Addressing challenges such as model interpretability, algorithmic bias, and data privacy will be pivotal for broader clinical adoption.
In conclusion, this study demonstrates the transformative potential of machine learning in cardiovascular risk prediction. By combining novel datasets, advanced algorithms, and clinically driven insights, the research sets a foundation for precision medicine, offering a scalable and effective solution for early diagnosis and improved patient outcomes globally. Keywords: Cardiovascular Disease, Machine Learning, Risk Prediction, Random Forest, Kasturba Dataset, Clustering, Early Diagnosis, Precision Medicine.
