Algoritma Naïve Bayes Dengan Backward Elimination Pada Dataset Breast Cancer
DOI:
https://doi.org/10.31599/tzsb5v61Keywords:
Backward Elimination, Breast Cancer, Classification, Naïve Bayes, Split ValidationAbstract
Cancer is a type of disease that is not recognized by most people, because some people affected by this disease do not know about cancer itself and do not do early detection of cancer, as a result most cancers are found at an advanced stage and are difficult to treat, thus placing a large burden on cancer sufferers. . Early detection of cancer, especially breast cancer is very important to do to overcome the very high risk of death in women caused by breast cancer. This study aims to help classify breast cancer based on data from routine patient examinations which are summarized in the coimbra breast cancer dataset and this data was donated to the UCI machine learning repository in 2018. The method used in the classification process in this study is backward elimination modeling for optimization accuracy as well as the naive Bayes algorithm and split validation validation to validate the model. The results of this study show an accuracy of 77.14%. These results indicate that the results of this study are good enough to help classify breast cancer.
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