重度精神疾病高暴力风险(SMI-V)精神病患者数据仓库与数据挖掘系统的开发——以泰国为例

Phichayasini Kitwatthanathawon, Jamnian Suravarangkul, Prachasan Vaenthaisong
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引用次数: 0

摘要

本研究旨在开发那空Ratchasima Rajanagarindra精神病院严重精神疾病高暴力风险(SMI-V)精神病患者的数据仓库和数据挖掘系统,并建立SMI-V精神病患者的分类模型。采用星型架构设计和数据库管理软件包进行数据仓库系统的开发。此外,利用数据挖掘技术,即Wrapper方法的特征选择,以及各种算法构建分类模型,将SMI-V患者与其他精神病患者进行分类。软件编程被用来创建基于网络的任务,具有图形用户界面(GUI),允许用户通过Internet与系统一起工作。数据仓库的设计和开发结果表明,它由一个事实表和六个维表组成。本研究分析了随机森林、随机树、J48决策树、ZeroR、OneR和多层感知器6种分类算法。开发分类模型的结果表明,采用Decision Tree J48算法的模型比其他模型具有更高的性能。关键词:数据仓库;数据挖掘;分类;具有高暴力风险的严重精神疾病;精神病人
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Development of Data Warehouse and Data Mining System for Serious Mental Illness with High Risk to Violence (SMI-V) Psychiatric Patients: A Case Study of Thailand
This study aimed to develop the data warehouse and data mining system for serious mental illness with high risk to violence (SMI-V) psychiatric patients of Nakhon Ratchasima Rajanagarindra Psychiatric Hospital, and develop a classification model for SMI-V psychiatric patients. Star schema design and database management software package were used for development of data warehouse system. Moreover, data mining technique, i.e., feature selection by Wrapper method, along with various algorithms were applied for constructing the classification model to classify SMI-V patients from other psychiatric patients. Software programming were used to create web-based task with graphical user interface (GUI) that would allow users to work with the system via Internet. The results of data warehouse design and development showed that it consisted of one fact table surrounding with six dimensional tables. There were six classification algorithms analyzed in this study which were Random Forest, Random Tree, Decision Tree J48, ZeroR, OneR, and Multilayer Perceptron. The results of developing classification models revealed that the model using Decision Tree J48 algorithm achieved higher performance than other models in comparison. Keywords: Data Warehouse; Data Mining; Classification; Serious Mental Illness with High Risk to Violence; Psychiatric Patients
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