C4.5: programs for machine learning
C4.5: programs for machine learning
Applied Survival Analysis: Regression Modeling of Time to Event Data
Applied Survival Analysis: Regression Modeling of Time to Event Data
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Machine Learning
Enhancement of a Chinese discourse marker tagger with C4.5
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
Expert Systems with Applications: An International Journal
Review: Data mining techniques and applications - A decade review from 2000 to 2011
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The purpose of this study is to determine new prognostic indexes for the differentiation of subgroups of breast cancer patients with the techniques of decision tree algorithms (C&RT, CHAID, QUEST, ID3, C4.5 and C5.0) and Cox regression analysis for disease-free survival (DFS) in breast cancer patients. A retrospective analysis was performed in 381 breast cancer patients diagnosed. Age, menopausal status, age of menarche, family history of cancer, histologic tumor type, quadrant of tumor, tumor size, estrogen and progesterone receptor status, histologic and nuclear grading, axillary nodal status, pericapsular involvement of lymph nodes, lymphovascular and perineural invasion, adjuvant radiotherapy, chemotherapy and hormonal therapy were assessed. Based on these prognostic factors, new prognostic indexes for C&RT, CHAID, QUEST, ID3, C4.5 and C5.0 and Cox regression were obtained. Prognostic indexes showed a good degree of classification, which demonstrates that an improvement seems possible using standard risk factors. We obtained that C4.5 has a better performance than C&RT, CHAID, QUEST, ID3, C5.0 and Cox regression to determine risk groups using Random Survival Forests (RSF).