Data mining with Random Forests as a methodology for biomedical signal classification

• Autorzy: Proniewska K.

Identyfikatory

DOI

10.1515/bams-2016-0005

• Języki publikacji: EN

Abstrakty

EN

As the contribution of specific parameters is not known and significant intersubject variability is expected, a decision system allowing adaptation for subject and environment conditions has to be designed to evaluate biomedical signal classification. A decision support system has to be trained in its desirable functionality prior to being used for patient monitoring evaluation. This paper describes a decision system based on data mining with Random Forests, allowing the adaptation for subject and environment conditions. This methodology may lead to specific system scoring by an artificial intelligence-supported patient monitoring evaluation system, which may help find a way of making decisions concerning future treatment and have influence on the quality of patients’ life.

Słowa kluczowe

EN

data mining; patient monitoring; Random Forests

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2016
• Tom: Vol. 12, no. 2
• Strony: 89--92
• Opis fizyczny: Bibliogr. 21 poz., wykr.

Twórcy

autor

Proniewska K.

• Jagiellonian University Medical College, Krakow, Poland

Bibliografia

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.