A Percentile Rank-Based Multi-Criteria Decision-Making Algorithm for Interpretable Hypertension Risk Stratification in Clinical Decision Support Systems

Abstract

Hypertension risk prediction remains a critical challenge in data-driven healthcare systems, particularly in low- and middle-income countries (LMICs). In many of these settings, timely monitoring and diagnosis remain difficult. Machine learning (ML) methods have been applied for hypertension risk prediction; however, their complexity and “black box” nature limit their integration into clinical decision support systems. This study presents a Percentile Rank-Based Multi-Criteria Decision Making (PRBMCDM) model for hypertension monitoring and risk stratification. Hypertension-related data were collected from public and private hospitals in the Kogi Eastern Senatorial District of Nigeria. The data were used to develop and validate the proposed model. The proposed algorithm calculates risk scores based on the percentile ranking of multiple risk criteria of each patient. The model is designed as a lightweight and interpretable framework suitable for deployment in resource-constrained digital health environments. A drop column method was used to rank features. The model performance was evaluated using 5-fold cross-validation. PRBMCDM achieved an AUC of 0.75, 0.70 and 0.72 high, medium, and low-risk respectively. The model demonstrated high interpretability, making it well-suited for deployment in clinical settings. The PRBMCDM algorithm offers a transparent, simple, and clinically relevant framework for hypertension risk stratification model. Key limitations of existing ML models were addressed. Future work will focus on validating PRBMCDM against established risk scores on larger and more divers dataset to further strengthen its clinical applicability.