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According to the most recent research, cancer is a worldwide problem affecting individuals of all ages and levels of income. Most women will get breast cancer at one point in their lives. So, every change in how cancer is found and how it will be treated makes it more likely that you won’t live a long life. The use of machine learning methods could have a big effect on predicting cancer and finding it early. Present work, the Wisconsin Cancer dataset is put into groups using two of the most popular machine learning methods. Using exactness, accuracy, recall, and ROC Area scores, the way these methods put things into groups was compared. The best results came from the Support Vector Machine method, proving to be accurate. With a 99.1% success rate, SVM is the best way to figure out what a prophet is saying. We think that this study leads us to the conclusion that SVM is the best method for prediction, and that DT works well with SVM.

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Breast Cancer Evaluation and Prevention Assessment by Employing ML and SVM Algorithms

  • 章节

  • 发表日期：

    2025年

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  • C. H. Rajakishore Babu,
  • Golla Saidulu,
  • K. Venkateswara Rao,
  • Mahesh Kotha,
  • Ratnababu Jyothi,
  • Mudimela Madhusudhan

摘要

According to the most recent research, cancer is a worldwide problem affecting individuals of all ages and levels of income. Most women will get breast cancer at one point in their lives. So, every change in how cancer is found and how it will be treated makes it more likely that you won’t live a long life. The use of machine learning methods could have a big effect on predicting cancer and finding it early. Present work, the Wisconsin Cancer dataset is put into groups using two of the most popular machine learning methods. Using exactness, accuracy, recall, and ROC Area scores, the way these methods put things into groups was compared. The best results came from the Support Vector Machine method, proving to be accurate. With a 99.1% success rate, SVM is the best way to figure out what a prophet is saying. We think that this study leads us to the conclusion that SVM is the best method for prediction, and that DT works well with SVM.