The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
INDUCTIVE AND BAYESIAN LEARNING IN MEDICAL DIAGNOSIS
Abstract
Although successful in medical diagnostic problems, inductive learning systems were not widely accepted in medical practice. In this paper two different approaches to machine learning in medical applications are compared: the system for inductive learning of decision trees Assistant, and the naive Bayesian classifier. Both methodologies were tested in four medical diagnostic problems: localization of primary tumor, prognostics of recurrence of breast cancer, diagnosis of thyroid diseases, and rheumatology. The accuracy of automatically acquired diagnostic knowledge from stored data records is compared, and the interpretation of the knowledge and the explanation ability of the classification process of each system is discussed. Surprisingly, the naive Bayesian classifier is superior to Assistant in classification accuracy and explanation ability, while the interpretation of the acquired knowledge seems to be equally valuable. In addition, two extensions to naive Bayesian classifier are briefly described: dealing with continuous attributes, and discovering the dependencies among attributes.
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Cites
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Cited by
Explaining prediction models and individual predictions with feature contributions
Štrumbelj, E., & Kononenko, I. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-013-0679-x · Cited by 2,286
Self-citationMachine learning for medical diagnosis: history, state of the art and perspective
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Self-citationPredictive data mining in clinical medicine: Current issues and guidelines
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Comprehensible classification models: a position paper
Freitas, A. A. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2594473.2594475 · Cited by 572
Leveraging uncertainty information from deep neural networks for disease detection
Leibig, C., Allken, V., Ayhan, M. S., Berens, P., & Wahl, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-17876-z · Cited by 510
Metadata
- Authors
- KONONENKO, I.
- Venue
- Applied Artificial Intelligence
- Publisher
- Informa UK Limited
- Published
- 1993-10
- Volume
- 7
- Issue
- 4
- Pages
- 317-337
- ISSN
- 0883-9514 (ISSN-L)
- 1087-6545
- OpenAlex
- W2022058268
- Cited by
- 302
- References
- 34
- FWCI
- 13.17
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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