Dissertation/ Thesis
Machine learning In breast cancer prognosis and prediction
| Title: | Machine learning In breast cancer prognosis and prediction |
|---|---|
| Authors: | Chowdhury, Shah Abul Hasnat, Faruqee, Golam Akbar, Hassan, Sayeed, Jawad, Golam Mostafa Chowdhury |
| Contributors: | Zaman, Shakila, Ashraf, Faisal Bin, Department of Computer Science and Engineering |
| Publisher Information: | BRAC University |
| Publication Year: | 2022 |
| Collection: | BRAC University, Bangladesh: Institutional Repository |
| Subject Terms: | Machine learning, Linear SVC, DT, ANN, KNN, Breast cancer, Human-computer interaction, Biomedical engineering--Computer simulation |
| Description: | Cataloged from PDF version of thesis. ; Includes bibliographical references (pages 31-33). ; This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. ; In the human body, cancer is a condition that causes cells to proliferate quickly and uncontrolled across the whole body. It has the ability to arise in any of the billions of cells that build up the human body. Human cells generally become divided and turn into new cells as the requirement for human body. When cells get harmed or turn aged, they perish, and young vesicle replace them. Cancer can take many forms. Cancer is normally designated after the limb or tissues in which it arises. For instance, kidney cancer starts in the kidney, blood cancer starts in the blood cells and breast cancer starts in the human breasts. Cancer in breast is the maximal prevalent and frequent disease in female population all over the world. The majority of women identified with breast cancers are just above 50 in age, but breast cancer may strike anybody at any age. In the developed world, in one out of every eight women is diagnosed with breast cancer. However, early detection can help to prevent deaths and save many lives. This paper focuses on prediction and prognosis of cancer in breast using ML models where the paper provides accuracy of the ML deep learning models in diagnostically identifying 569 patients where 212 malignant and 357 benign Fine Needle Aspirate ( FNAs) and its potential accuracy. Also, Recall and the feature numbers in the database is obtained, which is depicted visually. First of all, we have given an overview of ML and deep learning approaches including DT, KNN and Linear SVC and ANN. We examine their BC implications. The Wisconsin breast cancer database (WBCD) is a standard database for assessing results using multiple techniques. This data set shows features such as tumor radius, concavity, texture and fractal dimensions also defined the tumor as Benign or ... |
| Document Type: | thesis |
| File Description: | 45 pages; application/pdf |
| Language: | English |
| Relation: | ID 17301143; ID 17301085; ID 17201051; ID 19101638; https://hdl.handle.net/10361/21799 |
| Availability: | https://hdl.handle.net/10361/21799 |
| Rights: | Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. |
| Accession Number: | edsbas.B09E50DA |
| Database: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://hdl.handle.net/10361/21799# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Header | DbId: edsbas DbLabel: BASE An: edsbas.B09E50DA RelevancyScore: 772 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 772.276306152344 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Machine learning In breast cancer prognosis and prediction – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chowdhury%2C+Shah+Abul+Hasnat%22">Chowdhury, Shah Abul Hasnat</searchLink><br /><searchLink fieldCode="AR" term="%22Faruqee%2C+Golam+Akbar%22">Faruqee, Golam Akbar</searchLink><br /><searchLink fieldCode="AR" term="%22Hassan%2C+Sayeed%22">Hassan, Sayeed</searchLink><br /><searchLink fieldCode="AR" term="%22Jawad%2C+Golam+Mostafa+Chowdhury%22">Jawad, Golam Mostafa Chowdhury</searchLink> – Name: Author Label: Contributors Group: Au Data: Zaman, Shakila<br />Ashraf, Faisal Bin<br />Department of Computer Science and Engineering – Name: Publisher Label: Publisher Information Group: PubInfo Data: BRAC University – Name: DatePubCY Label: Publication Year Group: Date Data: 2022 – Name: Subset Label: Collection Group: HoldingsInfo Data: BRAC University, Bangladesh: Institutional Repository – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+SVC%22">Linear SVC</searchLink><br /><searchLink fieldCode="DE" term="%22DT%22">DT</searchLink><br /><searchLink fieldCode="DE" term="%22ANN%22">ANN</searchLink><br /><searchLink fieldCode="DE" term="%22KNN%22">KNN</searchLink><br /><searchLink fieldCode="DE" term="%22Breast+cancer%22">Breast cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Human-computer+interaction%22">Human-computer interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Biomedical+engineering--Computer+simulation%22">Biomedical engineering--Computer simulation</searchLink> – Name: Abstract Label: Description Group: Ab Data: Cataloged from PDF version of thesis. ; Includes bibliographical references (pages 31-33). ; This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. ; In the human body, cancer is a condition that causes cells to proliferate quickly and uncontrolled across the whole body. It has the ability to arise in any of the billions of cells that build up the human body. Human cells generally become divided and turn into new cells as the requirement for human body. When cells get harmed or turn aged, they perish, and young vesicle replace them. Cancer can take many forms. Cancer is normally designated after the limb or tissues in which it arises. For instance, kidney cancer starts in the kidney, blood cancer starts in the blood cells and breast cancer starts in the human breasts. Cancer in breast is the maximal prevalent and frequent disease in female population all over the world. The majority of women identified with breast cancers are just above 50 in age, but breast cancer may strike anybody at any age. In the developed world, in one out of every eight women is diagnosed with breast cancer. However, early detection can help to prevent deaths and save many lives. This paper focuses on prediction and prognosis of cancer in breast using ML models where the paper provides accuracy of the ML deep learning models in diagnostically identifying 569 patients where 212 malignant and 357 benign Fine Needle Aspirate ( FNAs) and its potential accuracy. Also, Recall and the feature numbers in the database is obtained, which is depicted visually. First of all, we have given an overview of ML and deep learning approaches including DT, KNN and Linear SVC and ANN. We examine their BC implications. The Wisconsin breast cancer database (WBCD) is a standard database for assessing results using multiple techniques. This data set shows features such as tumor radius, concavity, texture and fractal dimensions also defined the tumor as Benign or ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: thesis – Name: Format Label: File Description Group: SrcInfo Data: 45 pages; application/pdf – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: ID 17301143; ID 17301085; ID 17201051; ID 19101638; https://hdl.handle.net/10361/21799 – Name: URL Label: Availability Group: URL Data: https://hdl.handle.net/10361/21799 – Name: Copyright Label: Rights Group: Cpyrght Data: Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. – Name: AN Label: Accession Number Group: ID Data: edsbas.B09E50DA |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.B09E50DA |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Linear SVC Type: general – SubjectFull: DT Type: general – SubjectFull: ANN Type: general – SubjectFull: KNN Type: general – SubjectFull: Breast cancer Type: general – SubjectFull: Human-computer interaction Type: general – SubjectFull: Biomedical engineering--Computer simulation Type: general Titles: – TitleFull: Machine learning In breast cancer prognosis and prediction Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chowdhury, Shah Abul Hasnat – PersonEntity: Name: NameFull: Faruqee, Golam Akbar – PersonEntity: Name: NameFull: Hassan, Sayeed – PersonEntity: Name: NameFull: Jawad, Golam Mostafa Chowdhury – PersonEntity: Name: NameFull: Zaman, Shakila – PersonEntity: Name: NameFull: Ashraf, Faisal Bin – PersonEntity: Name: NameFull: Department of Computer Science and Engineering IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2022 Identifiers: – Type: issn-locals Value: edsbas |
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