基于线性组合的骨质疏松疾病诊断分类模型研究

Diagnosis Classification Research for Disease of Osteoporosis Based on linear Combination model

为了有效降低骨质疏松疾病临床诊断误诊、漏诊率,分析骨质疏松诊断指标体系,综合粒子群神经网络和支持向量机两种学习型算法,以误差绝对值和达到最小为准则建立疾病诊断分类模型。采用蚌埠医学院第一附属医院骨科患者实际病例数据作为样本集,对模型进行训练和测试,使误差达到规定要求,并将仿真结果与单一诊断模型进行比较。实证分析表明组合模型诊断误差明显小于单一诊断模型。用基于神经网络和支持向量机的线性组合模型诊断原发性骨质疏松病情,是可行有效的方法。

In order to reduce the misdiagnosis rate of osteoporosis. We analyzed osteoporosis assessment indicator system. Comprehensive application neural networks and support vector machine learning algorithms, established linear combination diagnosis model to achieve the absolute minimum error. Then the combination model was used to compute a large number of actual data in orthopedics Department of first Affiliated Hospital of Bengbu Medical College. And the simulation results were compared with the single diagnostic model. Analysis shows the Diagnostic error of combined model is significantly less than single diagnostic models. The diagnosis of osteoporosis by using combination model based on neural network and support vector machine is feasible and effective.