Medical Diagnosis Problem Solving Based on the Combination of Genetic Algorithms and Local Adaptive Operations유전자 알고리즘 및 국소 적응 오퍼레이션 기반의 의료 진단 문제 자동화 기법 연구
- Other Titles
- 유전자 알고리즘 및 국소 적응 오퍼레이션 기반의 의료 진단 문제 자동화 기법 연구
- Authors
- Lee, Ki-Kwang; Han, Chang Hee
- Issue Date
- Jun-2008
- Publisher
- 한국지능정보시스템학회
- Keywords
- 의료진단; 분류; 혼용 유전자 알고리즘; 적응 오퍼레이션; 영역 에이전트
- Citation
- 지능정보연구, v.14, no.2, pp.193 - 206
- Indexed
- KCI
- Journal Title
- 지능정보연구
- Volume
- 14
- Number
- 2
- Start Page
- 193
- End Page
- 206
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/42918
- ISSN
- 2288-4866
- Abstract
- Medical diagnosis can be considered a classification task which classifies disease types from patient’s condition data represented by a set of pre-defined attributes. This study proposes a hybrid genetic algorithm based classification method to develop classifiers for multidimensional pattern classification problems related with medical decision making. The classification problem can be solved by identifying separation boundaries which distinguish the various classes in the data pattern. The proposed method fits a finite number of regional agents to the data pattern by combining genetic algorithms and local adaptive operations. The local adaptive operations of an agent include expansion, avoidance and relocation, one of which is performed according to the agent’s fitness value. The classifier system has been tested with well-known medical data sets from the UCI machine learning database, showing superior performance to other methods such as the nearest neighbor, decision tree, and neural networks.
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