Cancer Disease Prediction Using Naive Bayes,K-Nearest Neighbor and J48 algorithm

Cancer is a rising deadly disease which causes the death of 10% among all the diseases in Bangladesh. There are over 100 Types of cancer. Predicting Cancer plays a vital role for progressing data mining applications. Naive Bayes, k-nearest neighbor and j48 algorithm are used in this paper for predicting cancer disease. Naive Bayes is easy to build and really useful for very big dataset. K-nearest neighbor is uses dataset and create a dataset by separated into different classes and also predicting classification of new points. J48 Classifier are based on the decision Tree from training datasets, using the fact that each of them and data sets can be used for decision-making it into smallest subset. Weka tool is used for the purpose of measuring the accuracy of the cancer disease dataset including 09 types of cancer. 10-fold cross-validation is used for predicting cancer disease. In Naive Bayes the accuracy is 98.2%, k-nearest neighbor accuracy is 98.8% and j48 accuracy is 98.5%.