Research Article Open Access Double-Blind Peer Review

ENHANCING CANCER DETECTION WITH MACHINE LEARNING: DEVELOPMENT OF A MODEL USING IBM CLOUD TECHNOLOGY

Chikezie Emmanuel Okoro·Linda Ngozi Adebayo
Published 19 February 2025
Vol. 12, No. 4 (2024)
pp. 15-25
CC BY 4.0
  1. 1
    Chikezie Emmanuel Okoro
    Department of Computer Science, Ignatius Ajuru University of Education, Nigeria
    NG
  2. 2
    Linda Ngozi Adebayo
    Mathematical and Computer Sciences, University of Africa, Toru Orua, Nigeria
    NG

Cancer early detection is vital and has become a major research area for health and computing research. The collaboration between the two fields is essential as more efficient algorithms in machine learning is required. This paper is a demonstration of cancer detection using the IBM machine learning cloud in which different algorithms has been embedded. The Breast Cancer Wisconsin (Diagnostic) Dataset (WDBC) consisting of trained and untrained is used for the analysis. The result shows high rate of accuracy in detecting cancer. The results can also be viewed in different format, thereby making the IBM machine learning cloud an effective means of achieving a comprehensive simulation

JournalArtificial Intelligence, Machine Learning, and Data Science Journal
ISSN3064-8270
Volume / IssueVol. 12, No. 4 (2024)
Pages15-25
Published19 February 2025
DOI10.5281/zenodo.14892679
Access Open Access
LicenseCC BY 4.0 — reuse with attribution
PublisherKeith Publications
Okoro , C., Adebayo, L. (2025). ENHANCING CANCER DETECTION WITH MACHINE LEARNING: DEVELOPMENT OF A MODEL USING IBM CLOUD TECHNOLOGY. Artificial Intelligence, Machine Learning, and Data Science Journal, Vol. 12 No. 4, pp. 15-25. DOI: https://doi.org/10.5281/zenodo.14892679

 Submit Your Research to Artificial Intelligence, Machine Learning, and Data Science Journal

We invite original research articles, review papers, and case studies. Benefit from rigorous double-blind peer review, rapid decision within 4–8 weeks, DOI for every article, and worldwide open-access distribution.