Abstract
Breast Cancer is a major health problem and is one of the significant causes of death among women. Recurrence occurs when cancer returns after few years of treatment. To aid in the medical treatment and for clinical management, the early cancer diagnosis and prognosis have become the necessity in breast cancer recurrence. As the medical data is increasing with the advancement in medical technology, data mining facilitates to manage the data and provide useful medical progression and treatment of cancerous conditions. Various machine learning techniques can be used to support the doctors in effective and accurate decision making. In this paper, various classifiers have been tested for the prediction of type of breast cancer recurrence and the results show that neural networks outperform others. These findings will help the physicians in identifying the best features which can lead to breast recurrence.
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Goyal, K., Aggarwal, P., Kumar, M. (2020). Prediction of Breast Cancer Recurrence: A Machine Learning Approach. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_10
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DOI: https://doi.org/10.1007/978-981-13-8676-3_10
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