• Skip to main content
  • Skip to after header navigation
  • Skip to site footer
ERN: Emerging Researchers National Conference in STEM

ERN: Emerging Researchers National Conference in STEM

  • About
    • About AAAS
    • About the NSF
    • About the Conference
    • Partners/Supporters
    • Project Team
  • Conference
  • Abstracts
    • Undergraduate Abstract Locator
    • Graduate Abstract Locator
    • Abstract Submission Process
    • Presentation Schedules
    • Abstract Submission Guidelines
    • Presentation Guidelines
  • Travel Awards
  • Resources
    • Award Winners
    • Code of Conduct-AAAS Meetings
    • Code of Conduct-ERN Conference
    • Conference Agenda
    • Conference Materials
    • Conference Program Books
    • ERN Photo Galleries
    • Events | Opportunities
    • Exhibitor Info
    • HBCU-UP/CREST PI/PD Meeting
    • In the News
    • NSF Harassment Policy
    • Plenary Session Videos
    • Professional Development
    • Science Careers Handbook
    • Additional Resources
    • Archives
  • Engage
    • Webinars
    • ERN 10-Year Anniversary Videos
    • Plenary Session Videos
  • Contact Us
  • Login

Improving Invasive Breast Cancer Care Using Machine Learning Techniques

Graduate #14
Discipline: Data Science
Subcategory: Cancer Research
Session: 4
Room: Senate

Solangé Tchounwou - Tulane University
Co-Author(s): *, Solange S. Tchounwou 1, Clement G. Yedjou 2 , Jameka Grigsby 3 , Kearra Johnson 4 , and Paul B.Tchounwou 4



Breast cancer is the most common malignant in women worldwide. Women in the United States has a 1 in 8 chance of developing invasive forms of breast cancer during theirlifetime. Breast cancer arises in the lining cells (epithelium) of the ducts or lobules in theglandular tissue of the breast. The goal of the present study was to use ML application toundertake an extensive comparison of the invasive forms of breast cancer including, infiltratingductal carcinoma, infiltrating lobular carcinoma, and mucinous carcinoma. To achieve this goal,we used machine learning algorithms and collected scientific datasets of 334 breast cancerpatients available at https://www.kaggle.com/amandam1/breastcancerdataset and interpreted thisdataset based on the form of breast cancer, age, sex, tumor stages, surgery type, and survival rate.Among the 334 patients, 70% were diagnosed with infiltrating ductal carcinoma, 27% withinfiltrating lobular carcinoma, and 3% with mucinous carcinoma. Overall, 64 out of 334 breastcancer patients were in stage I, 189 patients in stage II, and 81 patients in stage III. Sixty-six (66)patients underwent lumpectomy, 67 patients underwent simple mastectomy, 96 patientsundergone modified radical mastectomy, and 105 patients underwent other types of surgery. Thesurvival rate was 83.4% for stage I, 79.1% for stage II, and 77% for stage III, respectively.Finding obtained from the present study demonstrated that machine learning provides large datathat holds great promise for improving breast cancer outcomes.

Funder Acknowledgement(s): This work was funded by the National Institutes of Health (NCI), Grant #1U54MD015929-01 at Jackson State University, Jackson, MS, United States, and FacultyResearch Award Program (FRAP) at Florida Agricultural and Mechanical University,Tallahassee, FL, United States.

Faculty Advisor: Dr. Clement G. Yedjou, clement.yedjou@famu.edu

Role: I helped collect scientific datasets of 334 breast cancer patients that were available at https://www.kaggle.com/amandam1/breastcancerdataset. Using this data I helped compare and interpret datasets based on the form of breast cancer, age, sex, tumor stages, surgery type, and survival rate.

Sidebar

Abstract Locators

  • Undergraduate Abstract Locator
  • Graduate Abstract Locator

This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

AAAS

1200 New York Ave, NW
Washington,DC 20005
202-326-6400
Contact Us
About Us

  • LinkedIn
  • Facebook
  • Instagram
  • Twitter
  • YouTube

The World’s Largest General Scientific Society

Useful Links

  • Membership
  • Careers at AAAS
  • Privacy Policy
  • Terms of Use

Focus Areas

  • Science Education
  • Science Diplomacy
  • Public Engagement
  • Careers in STEM

Focus Areas

  • Shaping Science Policy
  • Advocacy for Evidence
  • R&D Budget Analysis
  • Human Rights, Ethics & Law

© 2023 American Association for the Advancement of Science