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Utilizing Machine Learning to Predict Breast Cancer: One Step Closer to Bridging the Gap Between the Nature Versus Nurture Debate

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Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1 (FTC 2022 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 559))

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Abstract

with breast cancer, scientists have been trying to find the most effective solutions and treatments. Moreover, studies on genes through machine learning were conducted. By identifying the factor that influences breast cancer the most, this knowledge can be used to prevent or treat patients with breast cancer appropriately. Furthermore, the result of this experiment extends onto the debate of nature versus nurture. If the result concludes that Only Gene or Only Mutation has a stronger effect on tumors, then it weighs nature more in this debate. Likewise, if Only Others is the dominant factor, then this emphasizes the nurture more in this debate. Gathered data was processed and went through eight different machine learning algorithms to predict the tumor size and stage. The ‘Others’ was concluded as the most influential factor for the tumor. Among the ‘Others’, the type of breast surgery and the number of chemotherapy received were identified with the highest correlation with tumor size and stage. In conclusion, this solidifies the nurture’s stance on the debate. The data on the external effects and the usage of a developed machine learning model can be adopted to improve the experiment because they would increase the accuracy of the result.

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Authors and Affiliations

  1. Seoul Scholars International, 982-5, Daechi-dong, Gangnam-gu, Seoul, Republic of Korea

    Junhong Park & Miso Kim

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  1. Junhong Park
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  2. Miso Kim
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Correspondence to Junhong Park .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Park, J., Kim, M. (2023). Utilizing Machine Learning to Predict Breast Cancer: One Step Closer to Bridging the Gap Between the Nature Versus Nurture Debate. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_41

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