In this study, we describe an automatic classifier of patients with Heart Failure designed for a telemonitoring scenario, improving the results obtained in our previous works. Our previous studies showed that the technique that better processes the heart failure typical telemonitoring-parameters is the Classification Tree. We therefore decided to analyze the data with its direct evolution that is the Random Forest algorithm. The results show an improvement both in accuracy and in limiting critical errors.
Guidi, G., Pettenati, M.C., Miniati, R., Iadanza, E. (2013). Random forest for automatic assessment of heart failure severity in a telemonitoring scenario. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp.3230-3233). New York : IEEE [10.1109/EMBC.2013.6610229].
Random forest for automatic assessment of heart failure severity in a telemonitoring scenario
E. Iadanza
2013-01-01
Abstract
In this study, we describe an automatic classifier of patients with Heart Failure designed for a telemonitoring scenario, improving the results obtained in our previous works. Our previous studies showed that the technique that better processes the heart failure typical telemonitoring-parameters is the Classification Tree. We therefore decided to analyze the data with its direct evolution that is the Random Forest algorithm. The results show an improvement both in accuracy and in limiting critical errors.
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https://hdl.handle.net/11365/1215317