摘要
医学辅助诊断采用专家系统,针对数据挖掘在医学诊断应用,通过对经典决策树剪枝算法优缺点的分析,为满足诊断的多样性和灵活性,提出了一种基于多策略思想的剪枝算法。算法从用户需求的出发,根据不同的数据挖掘集接受对决策树模型描述性的参数,最终得到理想的决策树模型。实验结果表明,算法能够很好地平衡剪枝算法的精确性和复杂性,满足不同的医学诊断应用场景,保证了对不同数据挖掘集取得更好的适应性。应用模型建立了医学诊断辅助系统,实际试验表明达到了应用中理想的效果。
This paper focuses on the application of data mining in medical diagnosis,analyses the advantage and disadvantage of classic decision tree pruning algorithms,and presents a new pruning algorithm based on multi-strategy.This algorithm considers user’s needs,and can accept descriptive parameters of decision trees on different data mining set.Finally,it gets the ideal decision tree model.The results of experiments show that this algorithm balances the precision and complexity better,and meets the needs of medical diagnosis in different application context,and ensures better adaptability in different data mining set.This paper uses this model and builds the Medical Assistant Diagnosis System,and its application is almost satisfactory.
出处
《计算机仿真》
CSCD
北大核心
2010年第11期78-81,共4页
Computer Simulation
关键词
决策树
多策略
剪枝
辅助诊断
Decision tree
Multi-strategy
Pruning
Assistant diagnosis
