Integrating Explainable Machine Learning Models for Early Detection of Hypertension: A Transparent Approach to AI-Driven Healthcare

Hypertension is a major public health challenge globally, often undiagnosed until severe complications arise, highlighting the critical need for early and accurate risk prediction methods. Despite advances in machine learning (ML), many models remain black boxes, limiting clinical trust and adoption. This study addresses these gaps by evaluating and interpreting three ML classifiers—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes—for hypertension risk prediction, emphasizing both predictive performance and explainability. Using a comprehensive dataset of 4,187 participants, demographic and clinical factors, including age, gender, smoking status, blood pressure, BMI, glucose levels, and medication use, were analyzed. Descriptive statistics revealed significant differences between the at-risk and no-risk groups, particularly in terms of age, blood pressure, cholesterol levels, and diabetes prevalence. Chi-square and Welch's t-tests confirmed these distinctions (p <.001), underscoring the validity of the models' inputs. Model evaluation showed SVM as the most balanced classifier with an accuracy of 88.13% (95% CI [86.22%, 89.86%]) and substantial agreement (kappa = 0.7153). It achieved strong sensitivity (92.66%) and specificity (77.78%), alongside a favorable F1-score (0.9157), indicating robust true positive detection while minimizing false positives. KNN demonstrated high sensitivity (94.69%) but lower specificity (69.25%), with moderate overall accuracy (86.95%). Naïve Bayes, though highly sensitive (99.21%), suffered from poor specificity (34.63%), suggesting a high false-positive rate and imbalanced classification. McNemar's test indicated balanced errors only for SVM (p = 0.1036). Receiver Operating Characteristic (ROC) analysis revealed excellent discrimination for all models, with Naïve Bayes achieving an AUC of 0.953; however, this did not translate into practical reliability due to error imbalance. Explainable AI techniques, specifically SHAP values, elucidated key predictors in SVM, notably systolic and diastolic blood pressure, BMI, and heart rate, enhancing interpretability and stakeholder trust. According to the study, SVM offers the best trade-off between accuracy and interpretability for predicting hypertension risk. Integrating explainable ML models into clinical practice can improve early diagnosis, guide interventions, and inform health policies, supporting ethical, transparent, and effective AI-driven healthcare.