Publication: Defining the rehabilitation treatment programs for stroke patients by applying Neural Network and Decision Trees models
9
Issued Date
2015-01-01
Resource Type
Other identifier(s)
2-s2.0-84923035394
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
BMEiCON 2014 - 7th Biomedical Engineering International Conference. (2015)
Suggested Citation
Thunyanoot Prasertsakul, Panya Kaimuk, Warakorn Charoensuk Defining the rehabilitation treatment programs for stroke patients by applying Neural Network and Decision Trees models. BMEiCON 2014 - 7th Biomedical Engineering International Conference. (2015). doi:10.1109/BMEiCON.2014.7017422 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/35932
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Defining the rehabilitation treatment programs for stroke patients by applying Neural Network and Decision Trees models
Other Contributor(s)
Abstract
© 2014 IEEE. At present, patients whose have suffered from stroke in Thailand are increasing every year. Stroke impairments relate to many functions such as sensory, motor function, communication, visual and emotional function which depend on brain's lesion. Physical examinations and assessments are important for planning the rehabilitation programs. For this reason, there are several information for medical decision making. Missing some data for treatment planning may occur. To solve this problem, the proposed study used two algorithms to determine the proper rehabilitation treatment program. Artificial Neural Networks and Decision Trees models were considered. Sensitivity, specificity and accuracy values were computed to define the performance of both algorithms. The results of this study indicated that both techniques can apply for data classification and define the proper treatment programs. However, the results were shown that the specificity and accuracy of decision trees model were higher than neural network model.