k -均值聚类算法确定两岁以下儿童发育迟缓状态的实现

P. Sudarmadji, C. Bire
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引用次数: 1

摘要

印度尼西亚的发育迟缓患病率在世界上排名第四,这意味着印度尼西亚37%的幼儿发育迟缓。发育迟缓是由于长期缺乏营养摄入而导致的慢性营养缺乏问题,导致孩子的生长发育受到干扰,这是一个比他的年龄标准低或矮的孩子(侏儒),特别是在生命的第一个千金年龄。对母亲来说,生命的头1000天是从婴儿出生9个月算起,一直到2岁(0-23个月)。2018年基本卫生研究数据(RISKESDAS)显示,全国范围内的发育迟缓患病率为30.8%,其中短期患病率为19.3%,非常短,为11.5%,2018年最高的百分比是在努沙登加拉省。东部(42.6%)。NTT在发育不良状况方面在该省排名第一,用于确定发育不良状况的参数仅基于健康卡上的体重年龄(BB/U)。问题的紧迫性在于利用K-means聚类算法,根据WHO的标准特征对发育不良状态进行具体的分类。本研究的目的是利用聚类K-means算法方法对2岁以下儿童发育迟缓状况进行判别,得到分类结果。本研究的实现方法是CRISP-DM (Cross Industry Standard Process for Data mining)模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation Of K-Means Clustering Algoritm To Determine Stunted Status In Children Under Two Years Old
The stunting prevalence in Indonesia ranks fourth in the world, meaning that 37% of Indonesian toddlers are stunted. Stunting is a chronic nutritional deficiency problem caused by lack of nutritional intake for a long time, resulting in a disturbance to the child's growth, which is a lower or shorter child (dwarf) than the standard of his age, Especially in the first thousand golden age of life. The first thousand days of life is counted from the 9 months of a child in terms of mothers up to 2 years of life (0-23 months). The Data on the basic health Research (RISKESDAS) in 2018 shows the prevalence of stunting in the national sphere of 30.8%, consisting of a short prevalence of 19.3% and very short at 11.5%, and the highest percentage in 2018 is in Nusa Tenggara province. Eastern (42.6%). NTT ranked first in the province for stunting status and the parameters used to determine stunting status are based solely on the age of weight (BB/U), which is on the card to the Healthy (KMS). The urgency of the problem, is to classify the stunting status specifically based on the standard characteristics of the WHO use the K-means Clustering algorithm. The objective of the study is to obtain the classification results with the algorithm method of the Clustering K-means in determining the stunting status in children under the age of two years. The method of implementing this research is CRISP-DM (Cross Industry Standard Process for Data mining) model.
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