智能医疗系统中GA_SVM特征选择和参数优化

挂号是医疗过程最基本的单元,通常患者不知道自己病情,挂错科室的情况十分普遍,智能医疗系统的挂号功能很好地解决了这一难题,智能医疗系统利用医疗部门积累的海量病案文本进行训练和机器学习,对患者的病例特征进行分析将其分类到正确的病种,得出应挂的科室然后推荐给患者.而影响传统的支持向量机(SVM)文本分类的效率和准确率主要是特征值的提取和核函数参数的优化问题,由此提出了一种遗传算法(GA)和SVM相结合的文本分类方法,即把文本特征值和核函数的参数看作遗传算法中的一个染色体(一个个体),并进行二进制编码,对每一个个体进行选择、交叉、变异的遗传操作,得到最优的个体,最后通过支持向量机利用最优特征和最优参数进行文本分类.实验表明,该模型提高了患者智能诊断挂号的正确率,是一种较好的智能推荐诊断挂号算法.

Ga-Svm Based Feature Selection and Parameters Optimization in Intelligent Medical Systems

Medical registration is the most basic unit of the medical profession. Generally patients don't understand the condition of their illness. So choosing the wrong department is completely common. Intelligent medical system solves this problem very well. Intelligent medical system makes use of the massive medical record texts which the medical department accumulates to train and carry on machine learning, and to analyze the characteristics of the registration patient's medical record and to classify to the right disease. The patient is recommended to the appropriate department according to getting the department to be registered. The influence on the efficiency and accuracy of the traditional support vector machine (SVM) text classification of the intelligent medical system is the feature extraction and kernel function parameters optimization. Therefore, the method of the Genetic Algorithm (GA) combining with SVM is proposed. The text feature values and kernel function parameters together is viewed as a chromosome of the genetic algorithm that is an individual to carry on the binary encoding. The optimal individual is obtained by the genetic manipulation of the selection, crossover and mutation. Finally, text classification is operated by support vector machine using the optimal features and optimal parameters. The test result shows that this model improves the accuracy of intelligent diagnosis and is a good intelligent diagnosis registration algorithm.