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Comparative Studies | Computer Science & Engineering | India | Volume 12 Issue 5, May 2023
A Comparative Study of Analyzing Breast Cancer as Benign or Malignant using Machine Learning Algorithms
Nigel Jonathan Renny | Timothy William Richard | Dr. M. Maheswari
Abstract: Among the most prevalent cancers in women worldwide is breast cancer. Effective treatment and better patient outcomes depend on early identification and accurate diagnosis of breast cancer as Benign or Malignant. Machine learning algorithms have become effective tools for analysing breast cancer, offering excellent accuracy in the differentiation between benign and malignant tumours. In this study, we examine the performance of different machine learning algorithms for processing breast cancer data from the ?Wisconsin Diagnostic Breast Cancer (WDBC) dataset?, The MLAs including ?Logistic Regression, random forests, support vector machines(SVM) and artificial neural networks.? We will compare each algorithm's precision, sensitivity, and specificity in order to determine which one is the most efficient for diagnosing breast cancer. The findings of this study may have major applications for the development of more accurate and effective diagnostic and therapeutic approaches for breast cancer.
Keywords: Algorithms for cancer prediction, Machine Learning Algorithms, Cancer Prediction, Breast cancer, Logistic Regression, SVM, Support Vector Machines, Random Forests, Neural Networks
Edition: Volume 12 Issue 5, May 2023,
Pages: 194 - 199