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creased in all tests. With the best results with the
same value on the test without resampling, ROS, and
SMOTE. The best results are obtained with the accu-
racy 99.12%, recall 99.12%, precisions 99.13%, f1-
score 99.13% and AUC 0.988.
4 CONCLUSIONS
This study conducted a test for breast cancer predic-
tion. The use of resampling techniques such as ROS,
SMOTE, RUS, and SMOTE-Tomek is done to over-
come unbalanced data. The use of hyperparameter
tuning using a grid search with light gradient boost-
ing results in an increase and optimization of results.
Obtain the best results with accuracy 99.12%, re-
call 99.12%, precisions 99.13%, f1-score 99.13% and
AUC 0.988. In further research, testing can be done
with other breast cancer datasets or with other meth-
ods.
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