Comparison of decision tree, support vector machines, and Bayesian network approaches for classification of falls in Parkinson’s disease
Sarini, Sarini, McGree, James, White, Nicole, Mengersen, Kerrie, & Kerr, Graham (2015) Comparison of decision tree, support vector machines, and Bayesian network approaches for classification of falls in Parkinson’s disease. International Journal of Applied Mathematics and Statistics, 53(6), pp. 145-151.
Being able to accurately predict the risk of falling is crucial in patients with Parkinson’s dis- ease (PD). This is due to the unfavorable effect of falls, which can lower the quality of life as well as directly impact on survival. Three methods considered for predicting falls are decision trees (DT), Bayesian networks (BN), and support vector machines (SVM). Data on a 1-year prospective study conducted at IHBI, Australia, for 51 people with PD are used. Data processing are conducted using rpart and e1071 packages in R for DT and SVM, con- secutively; and Bayes Server 5.5 for the BN. The results show that BN and SVM produce consistently higher accuracy over the 12 months evaluation time points (average sensitivity and specificity > 92%) than DT (average sensitivity 88%, average specificity 72%). DT is prone to imbalanced data so needs to adjust for the misclassification cost. However, DT provides a straightforward, interpretable result and thus is appealing for helping to identify important items related to falls and to generate fallers’ profiles.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Journal Article|
|Keywords:||Bayesian network, decision tree, falls classification, na¨ıve Bayes classifier, Parkinson’s disease, support vector machines|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > HUMAN MOVEMENT AND SPORTS SCIENCE (110600) > Motor Control (110603)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > NEUROSCIENCES (110900) > Neurology and Neuromuscular Diseases (110904)
|Divisions:||Current > QUT Faculties and Divisions > Division of Research and Commercialisation
Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Schools > School of Exercise & Nutrition Sciences
|Copyright Owner:||Copyright 2015 by CESER PUBLICATIONS|
|Deposited On:||16 Dec 2015 05:56|
|Last Modified:||17 Dec 2015 04:23|
Repository Staff Only: item control page