Abstract
As much as data science is playing a pivotal role everywhere, health care also finds its prominent application. Breast Cancer is the top-rated type of cancer amongst women; which alone took away 627,000 lives. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be done in time. As a potential contributor to state-of-the-art technology development, data mining finds a multi-fold application in predicting Brest cancer. This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer. Breast Cancer Wisconsin data set from the UCI repository has been used as an experimental dataset while attribute clump thickness being used as an evaluation class. The performances of these twelve algorithms: Ada Boost M1, Decision Table, J-Rip, J48, Lazy IBK, Lazy K-star, Logistics Regression, Multiclass Classifier, Multilayer–Perceptron, Naïve Bayes, Random Forest, and Random Tree is analyzed on this data set.
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Abbreviations
- MAE:
-
Mean absolute error
- RMSE:
-
Root mean squared error
- RAE:
-
Relative absolute error
- RRSE:
-
Root relative squared error
- TP:
-
True Positive
- TN:
-
True Negative
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Kumar, V., Mishra, B.K., Mazzara, M., Thanh, D.N.H., Verma, A. (2020). Prediction of Malignant and Benign Breast Cancer: A Data Mining Approach in Healthcare Applications. In: Borah, S., Emilia Balas, V., Polkowski, Z. (eds) Advances in Data Science and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-15-0978-0_43
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