Intelligent risk stratification of hypertension based on ambulatory blood pressure monitoring and machine learning algorithms

• Published in: Physiological measurement Vol. 46; no. 3; pp. 35001 - 35016
• Main Authors: Deng, Muqing, Guo, Junsheng, Li, Boyan, Yang, Jingfen, Zhang, Xiaobo, Liang, Dandan, Wang, Yanjiao, Huang, Xiaoyu
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 31.03.2025
• Subjects: Adult; Aged; Algorithms; ambulatory blood pressure monitoring; Blood Pressure Monitoring, Ambulatory; Female; Humans; hypertension; Hypertension - diagnosis; Hypertension - physiopathology; Machine Learning; Male; Middle Aged; Risk Assessment - methods; risk stratification; ambulatory blood pressure monitoring; risk stratification; machine learning; hypertension
• ISSN: 0967-3334; 1361-6579; 1361-6579
• DOI: 10.1088/1361-6579/adbab0

Objective . Risk stratification of hypertension plays a crucial role in the treatment decisions and medication guidance during clinical practices. Although fruitful achievements have been reported on risk stratification of hypertension, the potential use of ambulatory blood pressure monitoring data is not well investigated. Different from single measuring blood pressure data, long-term blood pressure monitoring data can provide more comprehensive dynamical blood pressure information. Therefore, this paper proposes an intelligent hypertension risk stratification method based on ambulatory blood pressure monitoring data and improved machine learning algorithms. Approach . A total of 262 patients with hypertension are enrolled at People’s Hospital of Yangjiang, in which 93 subjects are with simple hypertension and 169 subjects have hypertension with complication. Time-domain features, frequency-domain features, nonlinear dynamics features and correlation features underlying time-varying ambulatory blood pressure monitoring data are extracted to obtain discriminative feature representations. Synthetic minority over-sampling algorithm is applied to solve the problem of data balancing. The particle swarm optimization combined with kernel extreme learning machine is employed for feature fusion and optimization. Main results . The proposed method can yield a diagnostic accuracy of 93.7%, 97.8%, and 98.4% under two-, five- and ten-fold cross-validation, which demonstrates hypertension risk stratification in an intuitive, quantizable manner using multi-dimensional feature representation and learning. Significance . The proposed method is expected to provide early warning for latent serious cardiovascular diseases before obvious symptoms are present.