Personalization and Prognostics: Synthesizing E-Commerce Clustering Algorithms with Healthcare Predictive Analytics

Abstract

The integration of personalized e-commerce clustering techniques into healthcare predictive analytics offers a novel approach to patient risk stratification and personalized intervention. This study explores the application of advanced clustering algorithms, traditionally used in consumer behavior analysis, to healthcare datasets for enhanced prognostic modeling. The purpose of this research is to investigate whether clustering-informed features can improve predictive accuracy and interpretability in healthcare risk prediction. We utilized K-Means, Hierarchical, and DBSCAN clustering algorithms to segment patient profiles based on clinical and behavioral attributes. These clusters were then incorporated as input features for predictive models, including Random Forest, XGBoost, and Convolutional Neural Networks for imaging-based data. The models were evaluated using accuracy, F1-score, and area under the receiver operating characteristic curve (AUROC). Explainable AI techniques were employed to ensure model interpretability and actionable insights for personalized healthcare recommendations. Results demonstrate that integrating clustering outputs significantly improved predictive performance, achieving an AUROC of 0.92 for cardiovascular risk prediction and an F1-score of 0.87 for early detection of cognitive impairment. Explainability analyses revealed distinct patient subgroups with actionable risk patterns, facilitating targeted interventions. This study highlights the potential of synthesizing e-commerce personalization methodologies with healthcare predictive analytics to advance consumer health informatics. The findings support the adoption of clustering-informed predictive models as a framework for personalized, data-driven healthcare decision-making.