Versions
- 2024-08-02 (2)
- 2024-08-02 (1)
Keywords
Logistic Regression
Decision Tree
Principal Component Analysis
Breast cancer
Abstract
ABSTRACT
Background: Low survival rates of breast cancer in developing countries are mainly due to the lack of early detection plans and adequate diagnosis and treatment facilities.
Objectives: This study aimed to apply machine learning techniques to recognize the most important breast cancer risk factors.
Methods: This case-control study included women aged 17-75 years who were referred to medical centers affiliated with Mashhad University of Medical Science between March 21, 2015, and March 19, 2016. The study had two datasets: one with 516 samples (258 cases and 258 controls) and another with 606 samples (303 cases and 303 controls). Written informed consent has been observed. Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and Principal Component Analysis (PCA) were applied using R studio software.
Results: Regarding the DT and RF, the most important features that impact breast cancer were family cancer, individual history of breast cancer, biopsy sampling, rarely consumption of a dairy, fruit, and vegetable meal, while in PCA and LR these features including family cancer, pregnancy number, pregnancy tendency, abortion, first menstruation, the age of first childbirth and childbirth number.
Conclusions: Machine learning algorithms can be used to extract the most important factors in the diagnosis of breast cancer in developing countries such as Iran.
Keywords: Random Forest, Logistic Regression, Decision Tree, Principal Component Analysis, Breast cancer
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