Efficient Breast Cancer Dataset Analysis Based on Adaptive Classifiers

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...

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Bibliographic Details
Published in:UHD Journal of Science and Technology 2024-04, Vol.8 (1), p.122-128
Main Authors: Al-Ani, Muzhir Shaban, Ali Kareem, Thikra, Nejres, Salwa Mohammed
Format: Article
Language:English
Subjects:
adaptive classifiers
breast cancer
healthcare analysis
percentage split evaluation technique
performance measures
Online Access:Get full text
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Summary: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.
ISSN:2521-4209
2521-4217
DOI:10.21928/uhdjst.v8n1y2024.pp122-128