Development and Validation of a Hypertension Risk Prediction Model Based on Particle Swarm Optimization-Support Vector Machine.

BACKGROUND

Hypertension is a prevalent health issue, especially among the elderly, and is linked to multiple complications. Early and accurate detection is crucial for effective management. Traditional detection methods may be limited in accuracy and efficiency, prompting the exploration of advanced computational techniques. Machine learning algorithms, combined with optimization methods, show potential in enhancing hypertension detection.

METHODS

In 2022, data from 1460 hypertensive and 1416 non-hypertensive individuals aged 65 and above were collected from the Lujingdong Outpatient Department of the Guangdong Second Traditional Chinese Medicine Hospital. Support Vector Machine (SVM) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) models were developed, validated using the holdout method, and evaluated based on sensitivity, specificity, positive predictive value (PPV), accuracy, G-mean, F1 score, Matthews correlation coefficient (MCC), and the area under the curve (AUC) of the receiver operating characteristic curve (ROC curve).

RESULTS

The PSO-SVM model outperformed the standard SVM, especially in sensitivity (93.9%), F1 score (0.838), and AUC-ROC (0.871).

CONCLUSION

The PSO-SVM model is effective for complex classifications, particularly in hypertension detection, providing a basis for early diagnosis and treatment.