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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (7): 1253-1260    DOI: 10.3785/j.issn.1008-973X.2021.07.004
    
ABC-SVM disease prediction method based on data fusion in community health care
Wei-qing PANG1(),Ning HE1,*(),Yan-hua LUO2,Xi YU3
1. School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
2. The First People's Hospital of Nanning, Nanning 530000, China
3. Qingpu District Center for Disease Control and Prevention, Shanghai 201799, China
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Abstract  

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.

Key words： community health care      disease prediction      support vector machine(SVM)      artificial bee colony(ABC)      cluster analysis     
Received: 18 May 2020      Published: 05 July 2021
CLC:  TP 391  
Fund:  国家自然科学基金资助项目（61661016）；广西无线宽带通信与信号处理重点实验室主任基金资助项目（GXKL06180101）；桂林电子科技大学研究生教育创新资助项目（2018YJCX23）
Corresponding Authors: Ning HE     E-mail: williampong@126.com;eicnhe@guet.edu.cn
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Wei-qing PANG
Ning HE
Yan-hua LUO
Xi YU
Cite this article:

Wei-qing PANG,Ning HE,Yan-hua LUO,Xi YU. ABC-SVM disease prediction method based on data fusion in community health care. Journal of ZheJiang University (Engineering Science), 2021, 55(7): 1253-1260.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.07.004     OR     https://www.zjujournals.com/eng/Y2021/V55/I7/1253


基于数据融合的ABC-SVM社区疾病预测方法

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.


关键词： 社区医疗,  疾病预测,  支持向量机(SVM),  人工蜂群(ABC),  聚类分析 
Fig.1 Disease prediction model in community
Fig.2 Flow chart for parameter optimization of SVM disease prediction model
序号 性别 年龄/岁 CREA/
(μmol·L?1) 		UA/
(μmol·L?1) 		APOA/
(g·L?1) 		APOB/
(g·L?1) 		GLU/
(mmol·L?1) 		LDL/
(mmol·L?1) 		UREA/
(mmol·L?1) 		CH/
(mmol·L?1)
注：肌酐(CREA)，尿酸(UA)，尿素(UREA)，血糖(GLU)，低密度脂蛋白胆固醇(LDL)，载脂蛋白A1(APOA)，载脂蛋白B(APOB)，总胆固醇(CH).
1 女 65 66 261 1.34 0.79 4.9 2.92 5.7 4.81
2 女 61 45 282 1.25 0.76 4.7 3.18 5.4 4.77
3 女 59 63 419 1.37 0.97 4.6 3.04 4.9 4.82
4 女 53 60 274 1.46 1.04 10.7 2.21 4.6 3.93
5 男 63 89 294 1.38 0.90 4.7 1.48 6.2 4.79
6 男 76 69 401 1.57 0.66 5.0 2.47 5.8 4.38
$\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $
Tab.1 Physiological data set used in experiment (partial samples)
样本编码 疾病类型
B4 B3 B2 B1
注：1表示样本患有该种疾病,0表示不患有该种疾病.
0x0 0 0 0 0
0x1 0 0 0 1
0x2 0 0 1 0
0x4 0 1 0 0
0x8 1 0 0 0
Tab.2 Encoding of five representative diseases combinations in samples
Fig.3 Visualization of illness in sample data
Fig.4 Principal components and their contribution rate of physiological index
Fig.5 Cluster dendrogram generated from samples
Fig.6 Visualization of cluster analysis result from samples
Fig.7 Comparison results of predictive effects among three diagnostic models
Fig.8 RMSE curves of different diagnostic models
Fig.9 Influence of parameter $c,g$ on ABC-SVM prediction accuracy
Fig.10 Comparison of optimization effects among three optimization algorithms
寻优算法 $c$ $g$ t/s η
GA-OPT $89.72$ $12.36$ $555.70\;$ $79.31\% {\rm{ }}$( $23/29$)
PSO-OPT $40.69$ $16.77$ $872.06\;$ $13.79\% {\rm{ }}$( $4/29$)
ABC-OPT $89.76$ $91.77$ $138.10\;$ $93.10\% {\rm{ }}$( $27/29$)
Tab.3 Optimization effects of different parameter optimization algorithms
Fig.11 RMSE comparison among different disease prediction method
样本编码 ${P}$ ${R}$ ${F}$
ABC-SVM PSO-SVM GA-SVM ABC-SVM PSO-SVM GA-SVM ABC-SVM PSO-SVM GA-SVM
0x0 1 0 1 1 0 1 1 0 1
0x4 0.91 0 0.77 1 0 0.83 0.96 0 0.83
0x6 1 0 0.33 0.5 0 0.4 0.67 0 0.4
0xB 1 0 1 1 0 1 1 0 1
0xC 0 0 0 0 0 0 0 0 0
0xE 0.89 0 0.86 1 0 0.8 0.94 0 0.8
0xF 1 0.14 1 1 1 0.86 1 0.24 0.86
Tab.4 Disease prediction results with different optimization algorithms
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