Efficient Breast Cancer Dataset Analysis Based on Adaptive Classifiers

Authors

  • Muzhir Shaban Al-Ani Department of Information Technology, College of Science and Technology, University of Human Development, Sulaymaniyah, KRG, Iraq
  • Thikra Ali Kareem Imam Ja'afar Al-Sadiq University, Salah Al-Din, Dijail
  • Salwa Mohammed Nejres Ministry of Higher Education and Scientific Research, Baghdad, Iraq

DOI:

https://doi.org/10.21928/uhdjst.v8n1y2024.pp122-128

Keywords:

Breast Cancer, Adaptive Classifiers, Performance measures, Percentage split evaluation technique, Healthcare analysis

Abstract

Many algorithms have been used to diagnose diseases, with some demonstrating good performance while others have not met expectations. Making correct decisions with the minimal possible errors is of the highest priority when diagnosing diseases. Breast cancer, being a prevalent and widespread disease, emphasizes the importance of early detection. Accurate decision-making regarding breast cancer is crucial for early treatment and achieving favorable outcomes. The percentage split evaluation approach was employed, comparing performance metrics such as precision, recall, and f1-score. Kernel Naïve Bayes achieved 100% precision in the percentage split method for breast cancer, while the Coarse Gaussian support vector machines achieved 97.2% precision in classifying breast cancer in 4-fold cross-validation.

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Published

2024-04-25

How to Cite

Al-Ani, M. S., Ali Kareem, T., & Nejres, S. M. (2024). Efficient Breast Cancer Dataset Analysis Based on Adaptive Classifiers. UHD Journal of Science and Technology, 8(1), 122–128. https://doi.org/10.21928/uhdjst.v8n1y2024.pp122-128

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Section

Articles