Abstract
Breast cancer has been recorded one of the most common disease among the females worldwide. It is the most common type of melanoma and responsible for the increasing mortality rate. The accurate and timely diagnosis of this fatal disease is particularly important to increase the chances of survival of the patients. There have been several implementations in this area, and numerous machine learning and soft computing algorithms have been proposed to analyze and detect the cancer and help the doctors to provide right medications on time. In this paper, a comparative analysis of prominent machine learning techniques is done, and their performance is evaluated. The techniques used for detecting and diagnosing the breast cancer are support vector machines (SVM), random forest, and k-nearest neighbor (k-NN). All experiments have been conducted with R programming software (data mining tool). The outcomes indicated that k-NN has achieved the highest accuracy (97.32%) in comparison with SVM, RF.
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Kumar, P., Bhatnagar, A., Jameel, R., Mourya, A.K. (2021). Machine Learning Algorithms for Breast Cancer Detection and Prediction. In: Das, S., Mohanty, M.N. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 202. Springer, Singapore. https://doi.org/10.1007/978-981-16-0695-3_14
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