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An interpretable data-driven approach for rules construction: Application to cardiovascular risk assessment.

, , , , , and . EMBC, page 2646-2649. IEEE, (2017)

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Assessment of cardiovascular risk based on a data-driven knowledge discovery approach., , , , , , and . EMBC, page 6800-6803. IEEE, (2015)Comparison of an interpretable data-driven approach with state of the art classifiers: Application to cardiovascular risk assessment., , , , , and . RTSI, page 1-5. IEEE, (2017)Ambient Assisted Living using Non-intrusive Smart Sensing and IoT for Gait Rehabilitation., , and . MetroXRAINE, page 489-494. IEEE, (2022)Strategies for personalized risk assessment for CVD management., , , , , and . BHI, page 344-347. IEEE, (2016)Improving clinical models based on knowledge extracted from current datasets: A new approach., , , , , and . EMBC, page 2295-2298. IEEE, (2016)A Clinical Interpretable Approach Applied to Cardiovascular Risk Assessment., , , , , , , and . EMBC, page 3252-3255. IEEE, (2018)Expert knowledge integration in the data mining process with application to cardiovascular risk assessment., , , , , , , and . EMBC, page 2538-2542. IEEE, (2015)An interpretable data-driven approach for rules construction: Application to cardiovascular risk assessment., , , , , and . EMBC, page 2646-2649. IEEE, (2017)Mutual information: a measure of dependency for nonlinear time series, , and . Physica A: Statistical Mechanics and its Applications, 344 (1): 326--329 (2004)Measuring and Controlling the Chaotic Motion of Profits., , , and . Int. J. Bifurc. Chaos, 19 (11): 3593-3604 (2009)