IJISCS (International Journal Of Information System and Computer Science)
Vol 5, No 1 (2021): IJISCS (International Journal of Information System and Computer Science)

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

Irma Handayani (Department of Informatics, University of Technology Yogyakarta)
Ikrimach Ikrimach (Department of Information System, University of Technology Yogyakarta)



Article Info

Publish Date
29 Jan 2021

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.

Copyrights © 2021






Journal Info

Abbrev

ijiscs

Publisher

Subject

Computer Science & IT

Description

The International Journal Information System and Computer Science (IJISCS) is a publication for researchers and developers to share ideas and results of software engineering and technologies. These journal publish some types of papers such as research papers reporting original research results, ...