Identification of knee osteoarthritis based on Bayesian network: Pilot study

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dc.contributor.author Sheng, Bo en
dc.contributor.author Huang, L en
dc.contributor.author Wang, X en
dc.contributor.author Zhuang, J en
dc.contributor.author Tang, Lihua en
dc.contributor.author Deng, C en
dc.contributor.author Zhang, Yanxin en
dc.date.accessioned 2019-10-08T09:35:17Z en
dc.date.issued 2019 en
dc.identifier.citation JMIR Medical Informatics 7(3):15 pages Article number e13562 2019 en
dc.identifier.issn 2291-9694 en
dc.identifier.uri http://hdl.handle.net/2292/48523 en
dc.description.abstract Background: Early identification of knee osteoarthritis (OA) can improve treatment outcomes and reduce medical costs. However, there are major limitations among existing classification or prediction models, including abstract data processing and complicated dataset attributes, which hinder their applications in clinical practice. Objective: The aim of this study was to propose a Bayesian network (BN)–based classification model to classify people with knee OA. The proposed model can be treated as a prescreening tool, which can provide decision support for health professionals. Methods: The proposed model’s structure was based on a 3-level BN structure and then retrained by the Bayesian Search (BS) learning algorithm. The model’s parameters were determined by the expectation-maximization algorithm. The used dataset included backgrounds, the target disease, and predictors. The performance of the model was evaluated based on classification accuracy, area under the curve (AUC), specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV); it was also compared with other well-known classification models. A test was also performed to explore whether physical fitness tests could improve the performance of the proposed model. Results: A total of 249 elderly people between the ages of 60 and 80 years, living in the Kongjiang community (Shanghai), were recruited from April to September 2007. A total of 157 instances were adopted as the dataset after data preprocessing. The experimental results showed that the results of the proposed model were higher than, or equal to, the mean scores of other classification models: .754 for accuracy, .78 for AUC, .78 for specificity, and .73 for sensitivity. The proposed model provided .45 for PPV and .92 for NPV at the prevalence of 20%. The proposed model also showed a significant improvement when compared with the traditional BN model: 6.3% increase in accuracy (from .709 to .754), 4.0% increase in AUC (from .75 to .78), 6.8% increase in specificity (from .73 to .78), 5.8% increase in sensitivity (from .69 to .73), 15.4% increase in PPV (from .39 to .45), and 2.2% increase in NPV (from .90 to .92). Furthermore, the test results showed that the performance of the proposed model could be largely enhanced through physical fitness tests in 3 evaluation indices: 10.6% increase in accuracy (from .682 to .754), 16.4% increase in AUC (from .67 to .78), and 30.0% increase in specificity (from .60 to .78). Conclusions: The proposed model presents a promising method to classify people with knee OA when compared with other classification models and the traditional BN model. It could be implemented in clinical practice as a prescreening tool for knee OA, which would not only improve the quality of health care for elderly people but also reduce overall medical costs. en
dc.publisher JMIR Publications en
dc.relation.ispartofseries JMIR Medical Informatics en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri https://www.jmir.org/about/editorialPolicies#authorSelfArchivePolicy en
dc.rights.uri https://creativecommons.org/licenses/by/4.0/ en
dc.title Identification of knee osteoarthritis based on Bayesian network: Pilot study en
dc.type Journal Article en
dc.identifier.doi 10.2196/13562 en
pubs.issue 3 en
pubs.volume 7 en
dc.rights.holder Copyright: The authors en
pubs.author-url https://medinform.jmir.org/2019/3/e13562/ en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.subtype Article en
pubs.elements-id 777072 en
pubs.org-id Engineering en
pubs.org-id Mechanical Engineering en
pubs.org-id Science en
pubs.org-id Exercise Sciences en
pubs.number e13562 en
pubs.record-created-at-source-date 2019-07-23 en
pubs.online-publication-date 2019-07-18 en
pubs.dimensions-id 31322132 en


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