Key result
Tuned Gradient Boosting predicts hypertension risk with an F1-score of ~1.00, driven by blood pressure history.
Why the study?
Hypertension is a major contributor to cardiovascular morbidity, yet community screening often relies on late or opportunistic measurements.
Does a tuned Gradient Boosting algorithm improve hypertension risk prediction accuracy and F1-score compared to Random Forest, XGBoost, Logistic Regression, and Support Vector Machine in a public dataset of 1,985 patient records?
Observational (n=1,985)
No
Does a tuned Gradient Boosting algorithm improve hypertension risk prediction accuracy and F1-score compared to Random Forest, XGBoost, Logistic Regression, and Support Vector Machine in a public dataset of 1,985 patient records?
Tuned Gradient Boosting achieved near-perfect accuracy (F1-score 0.9976, ROC-AUC 1.0000) for hypertension risk prediction on a public dataset, though ablation analysis revealed heavy reliance on prior blood pressure history, necessitating external clinical validation before deployment.
Machine learning models may aid hypertension risk prediction in research; leaves open external validation before any clinical use.
Hypertension remains a major contributor to cardiovascular morbidity, yet community screening often relies on late or opportunistic measurements. This study compares Logistic Regression, Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine for hypertension risk prediction using a public dataset of 1,985 patient records and ten demographic, clinical, and lifestyle predictors. Numerical features were standardized, categorical features were one-hot encoded, and missing medication values were imputed using the mode. Models were evaluated using an 80:20 stratified train-test split and 5-fold cross-validation. The best model was optimized with RandomizedSearchCV and examined through feature importance and ablation study. Tuned Gradient Boosting achieved the highest cross-validation F1-score of 0.9994 and ROC-AUC of 1.0000. On 397 test records, it obtained 0.9975 accuracy, 1.0000 precision, 0.9951 recall, 0.9976 F1-score, and 1.0000 ROC-AUC, with only one false negative. Ablation showed that removing blood pressure history reduced test F1-score to 0.7681, confirming its dominant contribution. The model is promising for screening support, but external clinical validation is required before diagnostic use.
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Vhaldino et al. (2026) conducted an observational in Hypertension (n=1,985). Tuned Gradient Boosting machine learning model vs. Random Forest, XGBoost, Logistic Regression, and Support Vector Machine was evaluated on Hypertension risk prediction performance (F1-score on independent test data). Tuned Gradient Boosting achieved a test F1-score of 0.9976 and ROC-AUC of 1.0000 for hypertension risk prediction, though removing blood pressure history reduced the F1-score to 0.7681.
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