Publisher
Springer Science and Business Media LLC
Reference80 articles.
1. Aamir S, Rahim A, Aamir Z, Abbasi SF, Khan MS, Alhaisoni M, Khan MA, Khan K, Ahmad J. Predicting breast cancer leveraging supervised machine learning techniques. Comput Math Methods Med. 2022;16:2022.
2. Aavula R, Bhramaramba R. XBPF: an extensible breast cancer prognosis framework for predicting susceptibility, recurrence and survivability. Int J Eng Adv Technol. 2019;8(5):2249–8958.
3. Abdar M, Zomorodi-Moghadam M, Zhou X, Gururajan R, Tao X, Barua PD, Gururajan R. A new nested ensemble technique for automated diagnosis of breast cancer. Pattern Recogn Lett. 2020;1(132):123–31.
4. Abiodun MK, Misra S, Awotunde JB, Adewole S, Joshua A, Oluranti J. Comparing the performance of various supervised machine learning techniques for early detection of breast cancer. In: International Conference on Hybrid Intelligent Systems 2021 Dec 14 (pp. 473–482). Springer, Cham.
5. Akay MF. Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst Appl. 2009;36(2):3240–7.
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