残疾人康复数据挖掘与辅助决策系统的设计与实现

发布时间：2018-06-01 06:27

本文选题：残疾人康复 + 辅助决策　；参考：《浙江大学》2015年硕士论文

【摘要】：随着信息技术的日益发展,我们进入了一个海量数据时代,如何从这些繁杂的数据中获得必要的信息并加以分析便成了一个不可避免的挑战。我国残疾人信息有着数量庞大,繁多,信息结构复杂等特点,为了更好的围绕残疾人社会保障体系与服务体系的建设,残疾人康复数据的挖掘与分析便起到了至关重要的作用。通过对相关数据的挖掘和分析可以加强社会、机构、个人对残疾人康复的了解,同时更可以为残疾人医疗康复保障政策的出台与落实提供相对应的技术与数据支撑。对于残疾患者及家人而言,可以通过对康复情况的预测与分析,更好的掌握康复进程,了解康复情况。综合上述情况,残疾人康复数据的挖掘与分析便成为了在残疾人康复领域的一个重要的研究课题。本文的研究目的是通过对残疾人康复数据的研究,设计并实现残疾人康复数据挖掘与辅助决策系统,主要包括康复情况分类与康复趋势预测两方面。在康复情况分类方面,应用svm、朴素贝叶斯、决策树等挖掘分类算法建立对应的决策模型,实现对残疾人康复结果的分类,并对分类的过程进行相对应的描述与展现。在康复信息预测方面,本文通过对数据的分析,应用相关算法预测了康复情况的趋势,对康复进程和方式起到了辅助决策作用。同时本文对分类挖掘结果的可视化进行了相应的设计与实现,使得结果更加直观与简洁易懂,对预测结果进行使用前后的对比,使得结论显现的更为具体生动。
[Abstract]:With the development of information technology, we have entered a mass of data era, how to obtain the necessary information from these complex data and analyze it has become an inevitable challenge. The information of disabled people in our country has the characteristics of large quantity, great variety and complex information structure. In order to better build the social security system and service system for the disabled, the mining and analysis of the rehabilitation data of the disabled has played a vital role. Through the mining and analysis of relevant data, we can strengthen the understanding of society, institutions and individuals on the rehabilitation of the disabled, and at the same time provide the corresponding technical and data support for the introduction and implementation of the policy of medical rehabilitation for the disabled. For the disabled patients and their families, we can better master the rehabilitation process and understand the rehabilitation situation through the prediction and analysis of the rehabilitation situation. Synthesizing the above situation, the data mining and analysis of disabled rehabilitation has become an important research topic in the field of disabled rehabilitation. The purpose of this paper is to design and implement the data mining and decision support system for disabled persons by studying the rehabilitation data of the disabled, including the classification of rehabilitation status and the prediction of rehabilitation trend. In the aspect of rehabilitation classification, the corresponding decision model is established by using SVM, naive Bayes, decision tree and other mining classification algorithms to realize the classification of rehabilitation results of disabled persons, and the corresponding description and presentation of the classification process are carried out. In the aspect of rehabilitation information prediction, this paper analyzes the data and uses the related algorithms to predict the trend of rehabilitation, which plays an auxiliary role in the decision-making of rehabilitation process and mode. At the same time, this paper designs and implements the visualization of the classification mining results, which makes the results more intuitive and simple and easy to understand. The comparison of the prediction results before and after the use of the results makes the conclusions more concrete and vivid.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP311.13;TP311.52

【参考文献】