Breast Cancer Prognosis using Machine Learning Ensemble Techniques
Disha H. Parekh1, Vishal Dahiya2
1Prof. Disha Harshadbhai Parekh, Department of Computer Science, Indus University, Ahmedabad, India.
2Prof. Dr. Vishal Dahiya, Department of Computer Science, Indus University, Ahmedabad, India.
Manuscript received on 31August 2022 | Revised Manuscript received on 02 September 2022 | Manuscript Accepted on 15 September 2022 | Manuscript published on 30 September 2022 | PP: 94-96 | Volume-11 Issue-3, September 2022 | Retrieval Number: 100.1/ijrte.C72880911322 | DOI: 10.35940/ijrte.C7288.0911322
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: According to WHO, breast cancer is the disease that affects people the most frequently and most dangerously in the world. Researchers are paying more attention to breast cancer because of how deadly it is and how early detection can prevent it. Since the advent of supervised machine learning algorithms, the early detection of breast cancer has advanced. The usage of several machine learning techniques as well as ensemble algorithms is demonstrated in the study. The outcomes were extremely precise, allowing for the best-possible cancer prediction. The paper’s modest goal is to save people suffering from the disease by enabling them to know if the detected tumour is cancerous or non-cancerous, being Malignant. It focuses on early diagnosis of breast cancer. This paper would be useful and aiding for those who are novel researchers in prediction and diagnosis of breast cancer using machine learning.
Keywords: Breast Cancer Prediction, Machine Learning, Ensemble XG Boost, AdaBoost.
Scope of the Article: Machine Learning