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

Irma Handayani; Ikrimach Ikrimach

doi:10.56327/ijiscs.v5i1.953

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

Irma Handayani⁽¹⁾, Ikrimach Ikrimach⁽²⁾,

(1) Department of Informatics, University of Technology Yogyakarta
(2) Department of Information System, University of Technology Yogyakarta

Corresponding Author

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.

Keywords

data mining; classification; k-nn algorithms; naive bayes algorithm, breast cancer

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