基于移动电信数据的个人健康风险预测与评估

发布时间：2019-03-16 10:17

【摘要】：随着通信业的发展和人们生活水平提高,移动电信数据越来越反映着人们的生活状况。为了研究移动电信数据与个人健康状况之间的内在关联,本文对移动电信数据进行跟踪采集,并对移动电信数据进行分类提取特征值处理,同时进一步通过改进了极限学习机框架来预测电信用户移动电信端的应用习惯和个人健康状况之间的内在联系,从而对移动电信客户进行健康风险预测和评估。本文以极限学习机理论为基础进行改进,并使用改进后的算法作为本文的核心算法。改进后的算法作为一种强有力的分类器可通过调节系统参数并根据移动电信数据特征向量来预测客户健康状况。本文从移动电信数据中寻求和用户自身相关的数据特征,进而从机器学习的角度出发,基于极限学习机框架提出改进算法,对基于移动电信数据的个人健康状况风险进行预测和评估,本文的研究致力对移动电信数据进行特征提取,同时对数据特征进行学习和分类识别。本文的主要工作包括:1.阐述了机器学习的基本理论思想及数学原理及三种数据挖掘算法的理论思想及数学原理,其中包含本文使用的极限学习机(ELM)框架,两种对照算法为支持向量机(SVM)算法及反向传播(BP)神经网络算法;2.针对移动电信数据的特点结合个人健康状况相关指标,采用基于移动电信数据的采集、处理和数据整理的方法,将移动电信数据特点与个人健康相关主要指标进行关联组建特征模型;3.提出了基于极限学习机框架的改进算法,用以处理移动电信数据,对个人健康风险进行预测与评估。改进后的算法通过采用隐藏节点数子选择过程,随机选用隐藏节点向量来训练网络,通过选择最佳的节点数来建立网络参数和选择训练模型。同时,本文通过在不同的测试数据下和多个复杂的条件下的仿真实验验证了改进后的算法在此类场景下精确高效的数据处理能力,并与其他两类算法进行了对比,证明改进后算法是一个高效准确且具有较低复杂度的数据识别算法。
[Abstract]:With the development of communication industry and the improvement of people's living standard, mobile telecommunication data more and more reflect people's living conditions. In order to study the inherent relationship between mobile telecommunication data and personal health, this paper tracks and collects mobile telecommunication data, and classifies and extracts the characteristic value of mobile telecommunication data. At the same time, the framework of extreme learning machine is improved to predict the inherent relationship between the application habits and personal health status of mobile telecommunication users, so as to predict and evaluate the health risk of mobile telecommunication customers. This paper is based on the theory of extreme learning machine, and uses the improved algorithm as the core algorithm of this paper. As a powerful classifier, the improved algorithm can predict the health status of customers by adjusting the system parameters and according to the feature vector of mobile telecommunication data. In this paper, from the point of view of machine learning, we propose an improved algorithm based on the framework of extreme learning machine, which is based on the mobile telecommunication data and the characteristics of the data related to the users themselves. The risk of personal health status based on mobile telecommunication data is predicted and evaluated. The research of this paper focuses on feature extraction of mobile telecommunication data, learning and classification of data features at the same time. The main work of this paper is as follows: 1. This paper expounds the basic theory and mathematical principle of machine learning and the theoretical and mathematical principles of three kinds of data mining algorithms, which includes the (ELM) framework of the extreme learning machine used in this paper. The two control algorithms are support vector machine (SVM) algorithm and backpropagation (BP) neural network algorithm. 2. According to the characteristics of mobile telecommunication data and the related indexes of personal health, this paper adopts the method of data collection, processing and data arrangement based on mobile telecommunication data. The characteristics of mobile telecommunication data are correlated with the main indicators of personal health to build a feature model. 3. An improved algorithm based on the framework of extreme learning machine is proposed to deal with mobile telecommunication data and to predict and evaluate personal health risks. The improved algorithm uses hidden node number sub-selection process, randomly selects hidden node vector to train the network, and sets up network parameters and training model by selecting the best number of nodes. At the same time, through the simulation experiments under different test data and multiple complex conditions, the improved algorithm is proved to be accurate and efficient in this kind of scenario, and is compared with the other two kinds of algorithms. It is proved that the improved algorithm is an efficient and accurate data recognition algorithm with low complexity.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R318;TN929.5

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