Open AccessComparison of Data Mining Methods Using the Naïve Bayes Algorithm and K-Nearest Neighbor in Predicting Immunotherapy Success
Author(s) -
Budi Harto,
Rino Rino
Publication year - 2019
Publication title -
tech-e/tech-e
Language(s) - English
Resource type - Journals
eISSN - 2598-7585
pISSN - 2581-1916
DOI - 10.31253/te.v2i2.139
Subject(s) - naive bayes classifier , k nearest neighbors algorithm , bayes' theorem , computer science , algorithm , data mining , machine learning , artificial intelligence , support vector machine , bayesian probability
tumor or cancer is a disease that is a problem for people who are increasing every year. This disease in both the early and final stages requires attention because in this disease sufferers have a large risk of death. along with the rapid development of technology, we can use the technology to facilitate in all fields one of which is to predict success in a therapy. Data mining is one of the techniques used by the author in testing the dataset used in this study to get the best algorithm between Naïve Bayes and the K-Nearest Neighbor algorithm by using the Rapid Miner S
tudio application and applying the best algorithm into the expected application or expert system. can help users predict the success of a therapy.