Abstract
Dementia with Lewy Bodies (DLB) is the second most common form of dementia, but diagnostic markers for DLB can be expensive and inaccessible, and many cases of DLB
are undiagnosed. This work applies machine learning techniques to determine the feasibility of distinguishing DLB from Alzheimer’s Disease (AD) using heterogeneous data features. The Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was first applied using a Leave-One-Out Cross-Validation protocol to a dataset comprising DLB and AD cases. Then, interpretable association rule-based diagnostic classifiers were obtained for distinguishing DLB from AD. The various diagnostic classifiers generated by this process had high accuracy over the whole dataset (mean accuracy of 94%). The mean accuracy in classifying their out-of-sample case was 80.5%. Every classifier generated consisted of very simple structure, each using 1-2 classification rules and 1-3 data features. As a group, the classifiers were heterogeneous and used several different data features. In particular, some of the
classifiers used very simple and inexpensive diagnostic features, yet with high diagnostic accuracy. This work suggests that opportunities may exist for incorporating accessible diagnostic assessments while improving diagnostic rate for DLB.
are undiagnosed. This work applies machine learning techniques to determine the feasibility of distinguishing DLB from Alzheimer’s Disease (AD) using heterogeneous data features. The Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was first applied using a Leave-One-Out Cross-Validation protocol to a dataset comprising DLB and AD cases. Then, interpretable association rule-based diagnostic classifiers were obtained for distinguishing DLB from AD. The various diagnostic classifiers generated by this process had high accuracy over the whole dataset (mean accuracy of 94%). The mean accuracy in classifying their out-of-sample case was 80.5%. Every classifier generated consisted of very simple structure, each using 1-2 classification rules and 1-3 data features. As a group, the classifiers were heterogeneous and used several different data features. In particular, some of the
classifiers used very simple and inexpensive diagnostic features, yet with high diagnostic accuracy. This work suggests that opportunities may exist for incorporating accessible diagnostic assessments while improving diagnostic rate for DLB.
Original language | English |
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Title of host publication | 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) |
Publisher | IEEE |
Pages | 4929-4933 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-7281-2782-8 |
ISBN (Print) | 978-1-7281-2783-5 |
DOIs | |
Publication status | Published (in print/issue) - 8 Sept 2022 |
Event | The 44th International Engineering in Medicine and Biology Conference (EMBC) - Glasgow, United Kingdom Duration: 11 Jul 2022 → 15 Jul 2022 |
Publication series
Name | |
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ISSN (Print) | 2375-7477 |
ISSN (Electronic) | 2694-0604 |
Conference
Conference | The 44th International Engineering in Medicine and Biology Conference (EMBC) |
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Country/Territory | United Kingdom |
City | Glasgow |
Period | 11/07/22 → 15/07/22 |
Bibliographical note
Funding Information:* This work was supported by the Newcastle National Institute for Health Research (NIHR) Biomedical Research Centre, hosted by Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University, and the European Union’s INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB) (Centre for Personalised Medicine, IVA 5036). The views and opinions expressed in this paper do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).
Publisher Copyright:
© 2022 IEEE.
Keywords
- Dementia
- dementia with Lewy bodies
- feature selection
- machine learning
- Alzheimer disease