Abstract
Breast cancer is a rapidly growing cancerous disease, which leads to the main cause of death in women. The early identification of breast cancer is essential for improving patients’ prognosis. The proposed work aims at identifying the relationships between the attributes of breast cancer datasets obtained from HCG Hospital, Bengaluru (India). The work focuses on identifying the effect of attributes on three different classes, which are metastasis, progression, and death using Apriori algorithm, an association rule mining technique. To analyze the relation among the attributes with the value it takes for a particular class, more detailed rules are generated using decision tree-based rule mining technique. Rules are selected for each class based on specific threshold set for confidence, lift, and support.
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Acknowledgments
This research work is carried out with the data provided by HealthCare Global Enterprises Ltd (HCG) Hospitals, Bengaluru, India, without any direct involvement of the patients. Ethical clearance has been taken from HealthCare Global Enterprises Ltd (HCG) Hospitals, Bengaluru, India.
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Mohan, K., Nair, P.C., Gupta, D., Nayar, R.C., Ram, A. (2021). Extraction of Relation Between Attributes and Class in Breast Cancer Data Using Rule Mining Techniques. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1199. Springer, Singapore. https://doi.org/10.1007/978-981-15-6353-9_31
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DOI: https://doi.org/10.1007/978-981-15-6353-9_31
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