Key result
The REMED classification algorithm showed similar accuracy to Bayesian methods and decision trees but was superior in its capacity to correctly classify sick patients for cardiovascular prognosis.
Why the study?
Does the REMED classification algorithm improve the correct classification of sick people based on ambulatory blood pressure variability compared to other algorithms?
Does the REMED classification algorithm improve the correct classification of sick people based on ambulatory blood pressure variability compared to other algorithms?
The REMED algorithm may offer an innovative and superior approach for correctly classifying patients with abnormal blood pressure variability for cardiovascular disease prognosis.
Should not yet change practice for BP variability prognosis; leaves open REMED's utility pending prospective validation.
Cardiovascular diseases constitute one of the main causes of mortality in the world, and machine learning has become a powerful tool for analysing medical data in the last few years. In this paper we present an interdisciplinary work based on an ambulatory blood pressure study and the development of a new classification algorithm named REMED. We focused on the discovery of new patterns for abnormal blood pressure variability as a possible cardiovascular risk factor. We compared our results with other classification algorithms based on Bayesian methods, decision trees, and rule induction techniques. In the comparison, REMED showed similar accuracy to these algorithms but it has the advantage of being superior in its capacity to classify sick people correctly. Therefore, our method could represent an innovative approach that might be useful in medical decision support for cardiovascular disease prognosis.
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Mena et al. (2009) studied Cardiovascular disease. REMED classification algorithm vs. Bayesian methods, decision trees, and rule induction techniques was evaluated on Accuracy and capacity to classify sick people correctly. The REMED classification algorithm showed similar accuracy to Bayesian methods and decision trees but was superior in its capacity to correctly classify sick patients for cardiovascular prognosis.
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