Key result
Classification and regression trees (CARTs) predicted systolic and diastolic blood pressure with an accuracy rate exceeding 90% and a training time of less than 0.5 seconds.
Population
Biological data collected from CM400 monitor for noninvasive continuous blood pressure measurement
Comparison
Classification and regression trees (CARTs) models vs Linear regression, ridge regression, support…
Design
Other
Authors
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May support efficient BP modeling in research; leaves open prospective clinical validation before any practice change.
CART models offer a highly accurate and computationally efficient approach for continuous noninvasive blood pressure prediction.
Zhang et al. (2018) studied Blood pressure prediction. Classification and regression trees (CARTs) vs. Linear regression, ridge regression, support vector machine and neural network was evaluated on Accuracy rate of predicting systolic BP and diastolic BP. Classification and regression trees (CARTs) predicted systolic and diastolic blood pressure with an accuracy rate exceeding 90% and a training time of less than 0.5 seconds.
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