Binary genetic algorithm for optimal joinpoint detection: Application to cancer trend analysis
- Authors
- Kim, Seongyoon; Lee S.; JUNG-IL CHOI; Cho H.
- Issue Date
- Feb-2021
- Publisher
- WILEY-BLACKWELL
- Keywords
- binary genetic algorithm; cancer incidence; joinpoint regression; SEER
- Citation
- STATISTICS IN MEDICINE, v.40, no.3, pp 799 - 822
- Pages
- 24
- Journal Title
- STATISTICS IN MEDICINE
- Volume
- 40
- Number
- 3
- Start Page
- 799
- End Page
- 822
- URI
- https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/6400
- DOI
- 10.1002/sim.8803
- ISSN
- 0277-6715
- Abstract
- The joinpoint regression model (JRM) is used to describe trend changes in many applications and relies on the detection of joinpoints (changepoints). However, the existing joinpoint detection methods, namely, the grid search (GS)-based methods, are computationally demanding, and hence, the maximum number of computable joinpoints is limited. Herein, we developed a genetic algorithm-based joinpoint (GAJP) model in which an explicitly decoupled computing procedure for optimization and regression is used to embed a binary genetic algorithm into the JRM for optimal joinpoint detection. The combinations of joinpoints were represented as binary chromosomes, and genetic operations were performed to determine the optimum solution by minimizing the fitness function, the Bayesian information criterion (BIC) and BIC
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 일반대학원 > 일반대학원 계산과학공학과 > 1. Journal Articles
- College of Science > 이과대학 수학 > 1. Journal Articles
Items in Scholar Hub are protected by copyright, with all rights reserved, unless otherwise indicated.