You Rou, Tao Qiaoli, Wang Siqi, Cao Lixing, Zeng Kexue, Lin Juncai, Chen Hao
School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, China.
Guangdong Provincial Hospital of Chinese Medicine, Guangzhou 510120, China.
Bioengineering (Basel). 2025 Feb 26;12(3):238. doi: 10.3390/bioengineering12030238.
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.
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).
The PSO-SVM model outperformed the standard SVM, especially in sensitivity (93.9%), F1 score (0.838), and AUC-ROC (0.871).
The PSO-SVM model is effective for complex classifications, particularly in hypertension detection, providing a basis for early diagnosis and treatment.
高血压是一个普遍存在的健康问题，尤其是在老年人中，并且与多种并发症相关。早期准确检测对于有效管理至关重要。传统检测方法在准确性和效率方面可能存在局限性，这促使人们探索先进的计算技术。机器学习算法与优化方法相结合，在提高高血压检测方面显示出潜力。
2022年，从广东省第二中医院鹿景东门诊部收集了1460名65岁及以上高血压患者和1416名非高血压患者的数据。开发了支持向量机（SVM）和粒子群优化支持向量机（PSO-SVM）模型，采用留出法进行验证，并基于灵敏度、特异度、阳性预测值（PPV）、准确度、G均值、F1分数、马修斯相关系数（MCC）以及受试者工作特征曲线（ROC曲线）的曲线下面积（AUC）进行评估。
PSO-SVM模型优于标准SVM，尤其是在灵敏度（93.9%）、F1分数（0.838）和AUC-ROC（0.871）方面。
PSO-SVM模型对于复杂分类有效，特别是在高血压检测中，为早期诊断和治疗提供了依据。
