Key result
A Lasso regression machine learning model accurately predicted patient-specific cardiac rehabilitation treatment paths with an average classification accuracy of 0.935 on the test set.
Observational (n=129)
No
Machine learning models, particularly Lasso regression, can accurately predict patient-specific cardiac rehabilitation programs based on admission characteristics.
Should not yet guide cardiac rehabilitation decisions; leaves open need for prospective validation.
Cardiac rehabilitation is a well-recognised nonpharmacological intervention that prevents the recurrence of cardiovascular events. Previous studies investigated the application of data mining techniques for the prediction of the rehabilitation outcome in terms of physical, but fewer reports are focused on using predictive models to support clinicians in the choice of a patient-specific rehabilitative treatment path. Aim of the work was to derive a prediction model for help clinicians in the prescription of the rehabilitation program.
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Lofaro et al. (2016) conducted an observational in Major cardiovascular event requiring cardiac rehabilitation (n=129). Lasso regression prediction model vs. Other machine learning algorithms (SVM, RF, FDA, C 5.0, CART) was evaluated on Average classification accuracy on test set. A Lasso regression machine learning model accurately predicted patient-specific cardiac rehabilitation treatment paths with an average classification accuracy of 0.935 on the test set.
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