Author(s)

Onur Aygörer ¹, Safiye Turgay ¹

Affiliation(s)

¹ Department of Industrial Engineering, Sakarya University, Sakarya, Turkey

Corresponding Author

Safiye Turgay

ABSTRACT

Breast cancer remains a significant global health concern, emphasizing the need for comprehensive investigations into its risk factors. This study employs decision tree modeling to analyze the impact of various factors on breast cancer incidence, aiming to contribute valuable insights for prevention and early intervention. Our findings reveal compelling patterns in breast cancer risk factors, shedding light on key variables that significantly influence susceptibility. Through rigorous decision tree modeling, we identify high-risk groups and highlight novel associations that warrant attention. The implications of these findings extend to both clinical practice and public health initiatives, providing a foundation for targeted prevention strategies and personalized healthcare approaches. This study not only enhances our understanding of breast cancer etiology but also underscores the utility of decision tree modeling in unraveling complex relationships within large datasets. As the field of breast cancer research continues to evolve, the insights presented here pave the way for future investigations, emphasizing the importance of tailored risk assessment and intervention strategies. 

KEYWORDS

Breast Cancer, Machine Learning, Feature Selection, Decision Tree, Rule Base

CITE THIS PAPER

Onur Aygörer, Safiye Turgay, Analyzing the Impact of Breast Cancer Risk Factors Using Decision Tree Modeling. Automation and Machine Learning (2023) Vol. 4: 39-48. DOI: http://dx.doi.org/10.23977/autml.2023.040305.

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