@inproceedings{d910194a821c4907825f9dee02f508ff,
title = "Creating prognostic systems by the mann-whitney parameter",
abstract = "We proposed two approaches to compute Mann-Whitney parameter based initial dissimilarities for the Ensemble Algorithm for Clustering Cancer Data (EACCD). These two approaches are non-parametric and produce robust prognostic systems. The breast cancer data from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute were used to demonstrate these two approaches. Results showed that our proposed methods generated prognostic systems with a comparable performance to the AJCC's cancer staging system.",
keywords = "Big data, Cancer, Dendrogram, Mann-Whitney parameter, Prognostic system, Survival",
author = "Huan Wang and Matthew Hueman and Qing Pan and Donald Henson and Arnold Schwartz and Li Sheng and Dechang Chen",
note = "Funding Information: This work was partially supported by the grants “Using Dendrograms to Create Prognostic Systems for Cancer” and “Creating Prognostic Systems for Cancer” sponsored by John P. Murtha Cancer Center Research Program at Walter Reed National Military Medical Center. Publisher Copyright: {\textcopyright} 2018 ACM.; 3rd IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2018 ; Conference date: 26-09-2018 Through 28-09-2018",
year = "2018",
doi = "10.1145/3278576.3278592",
language = "English (US)",
series = "Proceedings - 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "33--39",
booktitle = "Proceedings - 2018 IEEE/ACM International Conference on Connected Health",
}