A Hybrid Algorithm for Medical Diagnosis
A Hybrid Algorithm for Medical Diagnosis
复制标题
一种用于医疗诊断的混合算法
DOI：
10.1109/eurcon.2007.4400571
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
R. Potolea
中科院分区：
文献类型：
--
作者：
C. V. Bratu;Cristina Savin;R. Potolea
医学诊断和预后是分类问题的一个标志性例子。机器学习可以为从过去病例的描述中自动推断诊断规则提供宝贵的支持，使诊断过程更加客观和可靠。由于这个问题既涉及测试成本，也涉及错误分类成本，我们分析了ICET，这是文献中针对复杂成本问题最突出的方法。该混合算法试图避免传统贪婪归纳的缺陷，通过进化机制在可能的决策树空间中进行启发式搜索。我们的实现解决了初始ICET算法的一些问题，证明了它是所考虑问题的可行解决方案。
Medical diagnosis and prognosis is an emblematic example for classification problems. Machine learning could provide invaluable support for automatically inferring diagnostic rules from descriptions of past cases, making the diagnosis process more objective and reliable. Since the problem involves both test and misclassification costs, we have analyzed ICET, the most prominent approach in the literature for complex cost problems. The hybrid algorithm tries to avoid the pitfalls of traditional greedy induction by performing a heuristic search in the space of possible decision trees through evolutionary mechanisms. Our implementation solves some of the problems of the initial ICET algorithm, proving it to be a viable solution for the problem considered.