有效避孕工具的使用模式探索

E. W. Handamari
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引用次数: 0

摘要

确定接受者用于支持计划生育(“Keluarga Berencana”)的方法或避孕工具是一个问题。在选择避孕方法或避孕工具时,接受者必须考虑几个因素,即健康因素、伴侣因素和避孕方法。所使用的每种方法或避孕工具都有其优点或缺点。虽然一直在考虑利弊,但安全有效地控制生育仍然是一件困难的事情。因此,接受者改变了多次使用的避孕方法或避孕工具。确定有效避孕方法或避孕工具使用的变化规律,使受避孕者获得合适的避孕工具。研究避孕工具使用变化规律的方法之一是数据挖掘。数据挖掘是对大量数据进行有趣的模式提取。如果一个模式不是琐碎的、隐式的、以前未知的和有用的,那么这个模式就是有趣的。所呈现的模式应该易于理解,可以应用于将在一定程度上预测的数据,有用且新颖。在应用数据挖掘之前的早期阶段是使用k近邻算法确定因素的最短距离选择避孕工具。下一步,将数据挖掘应用于计划生育接受者对方法或避孕工具的使用变化数据,以期挖掘出接受者在使用方法或避孕工具时的行为模式相关信息。此外,从形成的模式来看,它可以用于有效避孕工具的使用决策。本研究得到的结果是,利用欧几里得距离的k个近邻可以用来确定家庭计划的接受者所拥有的属性与已有训练数据的相似度。在现有训练数据的基础上,利用数据挖掘的概念确定避孕工具的使用模式,如果该模式符合训练数据模式,则向计划生育的接受者提供建议。相反,如果模式不匹配,则系统不提供应该使用的避孕工具的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Usage Pattern Exploration of Effective Contraception Tool
Determination of methods or contraception tool used by acceptors to support the Family Planning (“Keluarga Berencana”) is a problematic. In choosing methods or contraception tool, the acceptor must consider several factors, namely health factor, partner factor, and contraceptive method. Each method or contraception tool which is used has its advantages or disadvantages. Although it has been considering the advantages and disadvantages, it is still difficult to control fertility safely and effectively. Consequently acceptor change the method or a contraception tool that is used more than once. In order acceptors get the appropriate contraception tool then the patterns of changing in the usage of effective methods or contraception tool is determined. One of the methods that can be used to look for the patterns of changing in the usage of contraception tool is data mining. Data mining is an interesting pattern extraction of large amounts of data. A pattern is said to be interesting if the pattern is not trivial, implicit, previously unknown, and useful. The patterns presented should be easy to understand, can be applied to data that will be predicted with a certain degree, useful, and new. The early stage before applying data mining is using k nearest neighbors algorithm to determine the factors shortest distance selecting the contraception tool. The next step is applying data mining to usage changing data of method or contraception tool of family planning acceptors which is expected to dig up information related to acceptor behavior pattern in using the method or contraception tool. Furthermore, from the formed pattern, it can be used in decision making regarding the usage of effective contraception tool. The results obtained from this research is the k nearest neighbors by using the Euclidean distance can be used to determine the similarity of attributes owned by the acceptors of Family Planning to the training data is already available. Based on available training data, it can be determined the usage pattern of contraceptiion tool with the concept of data mining, where the acceptors of Family Planning are given a recommendation if the pattern is on the training data pattern. Conversely, if the pattern is none match, then the system does not provide recommendations of contraception tool which should be used.
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