COMPARISON OF K-NEAREST NEIGHBOR AND NAÏVE BAYES FOR BREAST CANCER CLASSIFICATION USING PYTHON

Handayani, Irma and Ikrimach, Ikrimach (2021) COMPARISON OF K-NEAREST NEIGHBOR AND NAÏVE BAYES FOR BREAST CANCER CLASSIFICATION USING PYTHON. STMIK Pringsewu, https://ojs.stmikpringsewu.ac.id/index.php/ijiscs/article/view/953/pdf.

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Abstract

Classification is widely used to determine decisions according to new knowledge gained from processing past data using algorithms. The number of attributes can affect the performance of an algorithm. Several data mining methods that are widely used for classification include the K-Nearest Neighbor and naïve Bayes algorithm. The best algorithm for one data type is not necessarily good for another data type. It is even possible that a good algorithm will be horrendous for other data types. To overcome this issue, this study will analyze the accuracy of the K-Nearest Neighbor and Naïve Bayes algorithms for the classification of breast cancer. So that patients with existing parameters can be predicted which are malignant and benign breast cancer. This pattern can be used as a diagnostic measure so that the cancer can be detected earlier and is expected to reduce the mortality rate from breast cancer. The test results using kfold (k-10) cross validation, followed by confusion matrix of 455 data consisting of 284 data on cases of benign cancer, 171 data on malignant cancer cases, on the K-NN classifier were able to correctly classify 441 data with an accuracy rate of 0.97%, while the Naïve Bayes classifier was able to correctly classify 428 data with an accuracy rate of 0.94%. Keywords:data mining; classification; k-nn; naïve bayes; breast cancer.

Item Type:	Other
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Fakultas Sains Dan Teknologi > S1 Informatika
Depositing User:	Mrs Irma Handayani
Date Deposited:	27 Sep 2023 06:51
Last Modified:	27 Sep 2023 06:51
URI:	http://eprints.uty.ac.id/id/eprint/11507

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