Blood pressure classification accuracy

1. Machine learning improves blood pressure classification accuracy in silico (93.25% test accuracy).

   “adopted two strategies for train data balancing in the second phase of our study, which boosted the test accuracy to 93.25%.”

   In silicowith train data balancing

   Employing machine learning techniques for non-invasive blood pressure classification using photoplethysmography signals

   Physical and engineering sciences in medicine·6 Aug 2026

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1. Photoplethysmography predicts blood pressure classification accuracy in silico (91.13% accuracy).

   “extra trees classifier (ETC) model, combined with the SelectFromModel feature selection approach, achieved remarkable accuracy of 91.13% on the test dataset.”

   In silico

   Employing machine learning techniques for non-invasive blood pressure classification using photoplethysmography signals

   Physical and engineering sciences in medicine·6 Aug 2026

More on Photoplethysmography