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<p>Early assessment of arterial stiffness (AS) is crucial for cardiovascular disease prevention, with photoplethysmography (PPG) emerging&#xa0;as a promising&#xa0;and convenient detection approach. While current research continuously explores novel PPG features and leverages advanced machine learning techniques to enhance AS detection accuracy, this proliferation of features introduces the challenge of the dimensionality curse. Moreover, the variation in robustness among PPG features is often overlooked in feature selection process. To address these challenges, this study proposes a robustness-aware multi-objective feature selection (RA-MOFS) framework that integrates feature robustness into a cost function for jointly&#xa0;optimizing prediction accuracy and cost efficiency. The method incorporates adaptive evolutionary strategies with hybrid search mechanisms to generate a spectrum of cost-accuracy trade-off options, catering to diverse clinical scenarios. Experimental results demonstrate that RA-MOFS achieves superior Pareto front positioning and enhanced solution diversity compared to conventional multi-objective algorithms, showing significant improvements in both convergence and distribution metrics. Notably, the framework identified a representative solution utilizing only six high-robustness features that achieved a satisfactory AS prediction performance (MAE = 0.59&#xa0;m/s, r = 0.90) while meeting ARTERY Society validation standards (ME = -0.01&#xa0;m/s, SDE = 0.74&#xa0;m/s). Overall, this study provides an adaptable framework for PPG feature selection that enables viable AS assessment tools by balancing accuracy and practicality across diverse healthcare settings.</p>

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RA-MOFS: a robustness-aware multi-objective feature selection framework for PPG-based arterial stiffness prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月17日

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  • Shoutian Wu,
  • Zeyang Song,
  • Yunlong Li,
  • Xuelei Li,
  • Ting Sun,
  • Zuchang Ma,
  • Yining Sun

摘要

Early assessment of arterial stiffness (AS) is crucial for cardiovascular disease prevention, with photoplethysmography (PPG) emerging as a promising and convenient detection approach. While current research continuously explores novel PPG features and leverages advanced machine learning techniques to enhance AS detection accuracy, this proliferation of features introduces the challenge of the dimensionality curse. Moreover, the variation in robustness among PPG features is often overlooked in feature selection process. To address these challenges, this study proposes a robustness-aware multi-objective feature selection (RA-MOFS) framework that integrates feature robustness into a cost function for jointly optimizing prediction accuracy and cost efficiency. The method incorporates adaptive evolutionary strategies with hybrid search mechanisms to generate a spectrum of cost-accuracy trade-off options, catering to diverse clinical scenarios. Experimental results demonstrate that RA-MOFS achieves superior Pareto front positioning and enhanced solution diversity compared to conventional multi-objective algorithms, showing significant improvements in both convergence and distribution metrics. Notably, the framework identified a representative solution utilizing only six high-robustness features that achieved a satisfactory AS prediction performance (MAE = 0.59 m/s, r = 0.90) while meeting ARTERY Society validation standards (ME = -0.01 m/s, SDE = 0.74 m/s). Overall, this study provides an adaptable framework for PPG feature selection that enables viable AS assessment tools by balancing accuracy and practicality across diverse healthcare settings.