Breast Tumor Detection using Machine Learning Boosting Classifiers

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ID: 313262
2022
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Abstract
Breast cancer is the frequently found in women and the second greatest reason of death worldwide. As breast cancer is detected early, the ratio of survival rate increases because better therapy may be provided. ML algorithms are very vital in the early diagnosis of breast cancer. In this study, we purposed a Novel method that increases the accuracy and performance using these three different classifiers: Gradient Boost (GB), Ada Boost (ABC), and Extreme Gradient Boost (XGB). On the Public dataset WBC, we evaluated and compared the classifiers’ performance and accuracy. Because the chance of examples belonging to the majority of the class is relatively high, algorithms are far more likely to categorize new observations into the majority class in the classification phase. We address such a situation that True positive, false positive, precision, recall, F1 score, and accuracy are all used to evaluate the efficiency of each classifier. Experiments demonstrate that utilizing a boosting classifier improves the performance, with the Gradient Booster (GB) outperforming others in the WBC dataset.
Reference Key
imported_1777060114_69ebc9128953d Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muhammad Adnan
Journal Journal of Computing & Biomedical Informatics
Year 2022
DOI
10.56979/401/2022/64
URL
https://jcbi.org/index.php/Main/article/view/64
Keywords Keywords not found

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