Data mining with Random Forests as a methodology for biomedical signal classification
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Klaudia Proniewska
Abstract
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.
Author contributions: The author has accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
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©2016 by De Gruyter
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Articles in the same Issue
- Frontmatter
- Review
- Regeneration of knee joint menisci – methods review
- Original Articles
- Influence of neural network structure and data-set size on its performance in the prediction of height of growth hormone-treated patients
- Empirical wavelet transform-based delineator for arterial blood pressure waveforms
- Independent component analysis of EEG data for EGI system
- Model of dental braces for palatal suture expansion with tension analysis of two titanium alloys: Ti-13Nb-13Zr and Ti-6Al-4V
- A stepwise protocol for neural network modeling of persistent postoperative facial pain in chronic rhinosinusitis
- Short Communication
- Data mining with Random Forests as a methodology for biomedical signal classification
