Hybrid expert system for lifestyle recommendations in hypertensive patients

Introduction Hypertension management requires personalized lifestyle interventions, yet clinical decision-making often relies on manual assessment and limited decision-support tools. This study presents a hybrid clinical decision-support system that integrates unsupervised machine learning with rule-based expert reasoning to generate personalized lifestyle recommendations for hypertensive patients. Methods A real-world dataset of 615 clinical records obtained from routine healthcare services was analyzed. A preprocessing pipeline including data imputation, normalization, and dimensionality reduction was applied prior to patient stratification. Principal Component Analysis (PCA) preserved the dominant latent structure of the dataset, followed by K-Means clustering to identify patient profiles. The resulting clusters were integrated into a rule-based inference engine structured across six lifestyle intervention domains: physical activity, stress management, nutrition, sleep patterns, therapeutic adherence, and general health behaviors. Recommendations were generated using a dual-weighting strategy that prioritizes individual patient attributes while incorporating cluster-level contextual information. System performance was evaluated through blind expert validation involving cardiologists and clinical nutritionists. Results K-Means clustering identified three clinically interpretable patient profiles with a Silhouette coefficient of 0.5608. Agreement between automated recommendations and expert clinical consensus reached 78.3%, with a Cohen’s Kappa coefficient of 0.742, indicating substantial concordance. No statistically significant differences were observed between system outputs and expert judgments ( χ 2 = 8.347, p = 0.908). Discussion The findings demonstrate that combining unsupervised patient stratification with explicit clinical reasoning enables interpretable and scalable decision support for non-pharmacological hypertension management. This approach may be particularly valuable in healthcare environments with limited labeled data and constrained clinical resources.