Building a Medical Decision Support System for Colon Polyp Screening by Using Fuzzy Classification Trees.
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| Title: | Building a Medical Decision Support System for Colon Polyp Screening by Using Fuzzy Classification Trees. |
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| Authors: | I-Jen Chiang1 ijchiang@tmu.edu.tw, Ming-Jium Shieh2, Jane Yung-Jen Hsu3, Jau-Min Wong2 |
| Source: | Applied Intelligence. Jan/Feb2005, Vol. 22 Issue 1, p61-75. 15p. |
| Subjects: | POLYP (Computer system), Computer software development, Computer programming, Biochemic medicine, Artificial intelligence, Management information systems |
| Abstract: | To deal with highly uncertain and noisy data, for example, biochemical laboratory examinations, a classifier is required to be able to classify an instance into all possible classes and each class is associated with a degree which shows how possible an instance is in that class. According to these degrees, we can discriminate the more possible classes from the less possible classes. The classifier or an expert can pick the most possible one to be the instance class. However, if their discrimination is not distinguishable, it is better that the classifier should not make any prediction, especially when there is incomplete or inadequate data. A fuzzy classifier is proposed to classify the data with noise and uncertainties. Instead of determining a single class for a given instance, fuzzy classification predicts the degree of possibility for every class. Adenomatous polyps are widely accepted to be precancerous lesions and will degenerate into cancers ultimately. Therefore, it is important to generate a predictive method that can identify the patients who have obtained polyps and remove the lesions of them. Considering the uncertainties and noise in the biochemical laboratory examination data, fuzzy classification trees, which integrate decision tree techniques and fuzzy classifications, provide the efficient way to classify the data in order to generate the model for polyp screening. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 16815633 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Building a Medical Decision Support System for Colon Polyp Screening by Using Fuzzy Classification Trees. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22I-Jen+Chiang%22">I-Jen Chiang</searchLink><relatesTo>1</relatesTo><i> ijchiang@tmu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Ming-Jium+Shieh%22">Ming-Jium Shieh</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Jane+Yung-Jen+Hsu%22">Jane Yung-Jen Hsu</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Jau-Min+Wong%22">Jau-Min Wong</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Intelligence%22">Applied Intelligence</searchLink>. Jan/Feb2005, Vol. 22 Issue 1, p61-75. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22POLYP+%28Computer+system%29%22">POLYP (Computer system)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming%22">Computer programming</searchLink><br /><searchLink fieldCode="DE" term="%22Biochemic+medicine%22">Biochemic medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Management+information+systems%22">Management information systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To deal with highly uncertain and noisy data, for example, biochemical laboratory examinations, a classifier is required to be able to classify an instance into all possible classes and each class is associated with a degree which shows how possible an instance is in that class. According to these degrees, we can discriminate the more possible classes from the less possible classes. The classifier or an expert can pick the most possible one to be the instance class. However, if their discrimination is not distinguishable, it is better that the classifier should not make any prediction, especially when there is incomplete or inadequate data. A fuzzy classifier is proposed to classify the data with noise and uncertainties. Instead of determining a single class for a given instance, fuzzy classification predicts the degree of possibility for every class. Adenomatous polyps are widely accepted to be precancerous lesions and will degenerate into cancers ultimately. Therefore, it is important to generate a predictive method that can identify the patients who have obtained polyps and remove the lesions of them. Considering the uncertainties and noise in the biochemical laboratory examination data, fuzzy classification trees, which integrate decision tree techniques and fuzzy classifications, provide the efficient way to classify the data in order to generate the model for polyp screening. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1023/B:APIN.0000047384.85823.f6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 61 Subjects: – SubjectFull: POLYP (Computer system) Type: general – SubjectFull: Computer software development Type: general – SubjectFull: Computer programming Type: general – SubjectFull: Biochemic medicine Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Management information systems Type: general Titles: – TitleFull: Building a Medical Decision Support System for Colon Polyp Screening by Using Fuzzy Classification Trees. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: I-Jen Chiang – PersonEntity: Name: NameFull: Ming-Jium Shieh – PersonEntity: Name: NameFull: Jane Yung-Jen Hsu – PersonEntity: Name: NameFull: Jau-Min Wong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan/Feb2005 Type: published Y: 2005 Identifiers: – Type: issn-print Value: 0924669X Numbering: – Type: volume Value: 22 – Type: issue Value: 1 Titles: – TitleFull: Applied Intelligence Type: main |
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