An ensemble algorithm integrating consensus-clustering with feature weighting based ranking and probabilistic fuzzy logic-multilayer perceptron classifier for diagnosis and staging of breast cancer using heterogeneous datasets.
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| Title: | An ensemble algorithm integrating consensus-clustering with feature weighting based ranking and probabilistic fuzzy logic-multilayer perceptron classifier for diagnosis and staging of breast cancer using heterogeneous datasets. |
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| Authors: | Chatterjee, Subhashis1 (AUTHOR), Das, Ananya1 (AUTHOR) ananyadas16@gmail.com |
| Source: | Applied Intelligence. Jun2023, Vol. 53 Issue 11, p13882-13923. 42p. |
| Subjects: | Classification algorithms, Decision trees, Cancer diagnosis, Feature selection, Self-organizing maps, Missing data (Statistics), Early detection of cancer |
| Abstract: | Breast cancer is a major threat, predominantly affecting the female population. Staging of cancer enables early detection and prognosis of patients, leading to determination of efficient and accurate treatment. Consequently, simplified models are required to integrate heterogeneous data for deriving knowledge about patients for further treatment. To achieve this goal, developing machine learning based diagnostic techniques is the predominant need. Prompted by these facts, a novel diagnostic model for staging of breast cancer infusing ensemble clustering, feature weighting based ranking of clusters and ensemble classification into benign or malignant class is developed. The proposed work constitutes of five different phases: data pre-processing, feature selection, ensemble clustering, ensemble classification, and staging of cancer. This work first employs Multiple Imputation Chained Equation for imputing missing values, followed by proposed feature selection technique employing Association Rules, Classification and Regression Tree, and Fuzzy Logic. Subsequently, a coupled clustering and classification algorithm based on consensus is developed to cluster features from different datasets using Self-Organizing Map and Decision Tree. A hierarchical clustering based ranking of these clusters using Multilinear Regression and Modified Fuzzy Analytical Hierarchical Process is proposed to prioritize features. Next, a staged classifier is developed integrating Probabilistic Fuzzy Logic and Multilayer Perceptron followed by feature extraction based staging of cancer. Finally, proposed work is validated on four datasets with various performance metrics using different combinations of train-test dataset. Moreover, k-fold cross-validation is implemented to eliminate biasedness. The detailed analysis of results of this work showcases superiority over other state-of-art methods in literature. [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: 164005491 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An ensemble algorithm integrating consensus-clustering with feature weighting based ranking and probabilistic fuzzy logic-multilayer perceptron classifier for diagnosis and staging of breast cancer using heterogeneous datasets. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chatterjee%2C+Subhashis%22">Chatterjee, Subhashis</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Das%2C+Ananya%22">Das, Ananya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ananyadas16@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Intelligence%22">Applied Intelligence</searchLink>. Jun2023, Vol. 53 Issue 11, p13882-13923. 42p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+diagnosis%22">Cancer diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Self-organizing+maps%22">Self-organizing maps</searchLink><br /><searchLink fieldCode="DE" term="%22Missing+data+%28Statistics%29%22">Missing data (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Early+detection+of+cancer%22">Early detection of cancer</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Breast cancer is a major threat, predominantly affecting the female population. Staging of cancer enables early detection and prognosis of patients, leading to determination of efficient and accurate treatment. Consequently, simplified models are required to integrate heterogeneous data for deriving knowledge about patients for further treatment. To achieve this goal, developing machine learning based diagnostic techniques is the predominant need. Prompted by these facts, a novel diagnostic model for staging of breast cancer infusing ensemble clustering, feature weighting based ranking of clusters and ensemble classification into benign or malignant class is developed. The proposed work constitutes of five different phases: data pre-processing, feature selection, ensemble clustering, ensemble classification, and staging of cancer. This work first employs Multiple Imputation Chained Equation for imputing missing values, followed by proposed feature selection technique employing Association Rules, Classification and Regression Tree, and Fuzzy Logic. Subsequently, a coupled clustering and classification algorithm based on consensus is developed to cluster features from different datasets using Self-Organizing Map and Decision Tree. A hierarchical clustering based ranking of these clusters using Multilinear Regression and Modified Fuzzy Analytical Hierarchical Process is proposed to prioritize features. Next, a staged classifier is developed integrating Probabilistic Fuzzy Logic and Multilayer Perceptron followed by feature extraction based staging of cancer. Finally, proposed work is validated on four datasets with various performance metrics using different combinations of train-test dataset. Moreover, k-fold cross-validation is implemented to eliminate biasedness. The detailed analysis of results of this work showcases superiority over other state-of-art methods in literature. [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: 10.1007/s10489-022-04157-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 42 StartPage: 13882 Subjects: – SubjectFull: Classification algorithms Type: general – SubjectFull: Decision trees Type: general – SubjectFull: Cancer diagnosis Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Self-organizing maps Type: general – SubjectFull: Missing data (Statistics) Type: general – SubjectFull: Early detection of cancer Type: general Titles: – TitleFull: An ensemble algorithm integrating consensus-clustering with feature weighting based ranking and probabilistic fuzzy logic-multilayer perceptron classifier for diagnosis and staging of breast cancer using heterogeneous datasets. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chatterjee, Subhashis – PersonEntity: Name: NameFull: Das, Ananya IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 0924669X Numbering: – Type: volume Value: 53 – Type: issue Value: 11 Titles: – TitleFull: Applied Intelligence Type: main |
| ResultId | 1 |
