Early Risk Prediction of Breast Cancer among Patients using Machine Learning Techniques

Abstract

Breast cancer is still the leading cause of mortality among women today. The purpose of the current investigation is to properly forecast the risk of acquiring breast cancer. To forecast the risk based on gene expression data, A variety of machine learning methods may be utilized to help. When it comes to interpreting gene expression data, The most often encountered difficulty is determining which genes are the most informative. To maintain good health, every progress in cancer sickness diagnosis and prediction is essential. Since cancer treatment choices and survival rates are always evolving, A high degree of accuracy in cancer prediction is essential. Breast cancer prediction and early diagnosis may be considerably improved by ML approaches, which have developed a research hotspot & have been demonstrated to be a highly useful technology in the area of breast cancer. In this research, 3 algorithms are presented, one of which is a machine learning algorithm, which has the highest accuracy of the three algorithms analyzed. Using these algorithms, we can detect breast cancer at an early stage.

Note:
Funding Information: The work did not receive any funding.

Conflict of Interests: The authors declare that that they have no conflicts of interest to report regarding the present study. We confirm that we have no conflicts of interest to disclose.

Keywords: Breast Cancer, Prediction, preprocessing, Diagnostic, Machine learning (ML), Artificial Intelligence (AI)