基于数据融合的ABC-SVM社区疾病预测方法

ABC-SVM disease prediction method based on data fusion in community health care

针对现有社区医疗服务中的疾病预测方法存在数据利用率低、疾病分析类型单一、自动化程度差、疾病预测效果不理想等不足,提出在物联网大数据环境下可用于社区医疗的健康数据融合及疾病预测方法.通过主成分分析(PCA)和聚类分析对社区中居民的生理指标数据进行特征提取;结合人工蜂群(ABC)算法构造支持向量机(SVM)非线性分类器对数据进行特征级融合分析并预测潜在疾病.实验结果表明,所提方法的疾病识别准确率达到93.10%,相较于传统SVM方法和BP神经网络方法分别提高17.24%和72.41%.该方法能够在提高数据利用率、降低计算资源消耗的前提下有效识别多种潜在疾病,可实现疾病早发现、早预防、早治疗;可广泛应用于社区健康管理、老年社区监护甚至临床医疗.

Existing disease prediction methods in community health service stilled have some defects such as low data utilization,single type disease,poor automation and unsatisfactory disease prediction effect.A health data fusion and disease prediction approach used in community healthcare in big data and Internet of Things(IoT)environment was proposed to solve these problems.Principal component analysis(PCA)and cluster analysis were used to extract features from the physiological data of residents in communities.The artificial bee colony(ABC)algorithm was used to construct a support vector machine(SVM)non-linear classifier to fuse the features data to predict many potential diseases.Experimental results show that the disease diagnostic accuracy of the proposed method is 93.10%,which is 17.24%higher than traditional SVM method and 72.41%higher than BP neural network.This method can effectively identify potential diseases under the premise of improving data utilization and reducing computing resource consumption,which makes early detection,prevention and treatment of diseases possible.It can be widely applied in community healthcare,elderly monitoring,clinical medicine in hospital.