@article{MAKHILLJEAS201813415541,
    title = {Effective Prediction Model for Colorectal Cancer using Decision
Tree and Clustering},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {4},
    pages = {903-909},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.903.909},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.903.909},
    author = {Min Soo and},
    keywords = {Colorectal Cancer (CRC),bowel cancer,decision tree,clustering,J48,situation,treatment},
    abstract = {Recently, data mining techniques have been utilized in various fields in order to effectively classify
and analyze the information desired by a large number of data. Decision trees and clustering are basic and
widely used techniques. We will use decision trees and clustering to analyze colon cancer and predict effective
treatment. So, we will analyze the decision tree and clustering in detail to predict effective treatment and then
study better treatment. In order to use various algorithms such as J48 decision trees and to selectively use the
optimal algorithm for a specific situation, the fitness of the algorithm for each situation. In this study, we
evaluated the performance of the algorithm based on the results of 10 cross validation using decision trees and
explained the treatment prediction of colon cancer based on the result of clustering. We analyzed the
performance of the algorithm and the results of the experimental data.}
    }