根据年龄对BPJS患者诊断分类的应用采用Clustering方法(案例研究:

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Information and Learning Technology Pub Date : 2022-03-13 DOI:10.32938/jitu.v2i1.1036
Leny Widiya Pa
{"title":"根据年龄对BPJS患者诊断分类的应用采用Clustering方法(案例研究:","authors":"Leny Widiya Pa","doi":"10.32938/jitu.v2i1.1036","DOIUrl":null,"url":null,"abstract":"The hospital is a health service institution that provides complete individual health services that provide inpatient services. Because the large number of patient data in the hospital makes it difficult for the administration to process existing data and information, the authors want to group patient data at the hospital to produce information. and can also facilitate the hospital in providing information to patients. Cluster is finding a collection of objects in a group that are the same (or have a relationship) with others and different (or not related) with objects in other groups. The purpose of cluster analysis is to minimize the distance within the cluster and maximize the distance between clusters. And the Centroid on the cluster is the center point. From the tests carried out using the clustering method with the k-means algorithm, it can be seen that cluster 2 of the criteria for disease symptoms, diagnosis results, age, group that has the highest set / value and the most patient diagnosis data, namely in Cluster 1 totaling 825. Patient data with diagnosis results using BPJS on Symptoms of High Fever and the diagnosis is Asthma, then age is> 60 years. and the most intermediate data on patients who experience delinquency, namely in Cluster 1 amounted to 595 patient data with a diagnosis using BPJS on Symptoms of High Fever and the diagnosis is anemia, then the age used is> 60 years.","PeriodicalId":51872,"journal":{"name":"International Journal of Information and Learning Technology","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2022-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Penerapan Data Mining Pengelompokan Hasil Diagnosa Pasien BPJS Berdasarkan Usia Menggunakan Metode Clustering (Studi Kasus: RSU Bidadari Binjai)\",\"authors\":\"Leny Widiya Pa\",\"doi\":\"10.32938/jitu.v2i1.1036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The hospital is a health service institution that provides complete individual health services that provide inpatient services. Because the large number of patient data in the hospital makes it difficult for the administration to process existing data and information, the authors want to group patient data at the hospital to produce information. and can also facilitate the hospital in providing information to patients. Cluster is finding a collection of objects in a group that are the same (or have a relationship) with others and different (or not related) with objects in other groups. The purpose of cluster analysis is to minimize the distance within the cluster and maximize the distance between clusters. And the Centroid on the cluster is the center point. From the tests carried out using the clustering method with the k-means algorithm, it can be seen that cluster 2 of the criteria for disease symptoms, diagnosis results, age, group that has the highest set / value and the most patient diagnosis data, namely in Cluster 1 totaling 825. Patient data with diagnosis results using BPJS on Symptoms of High Fever and the diagnosis is Asthma, then age is> 60 years. and the most intermediate data on patients who experience delinquency, namely in Cluster 1 amounted to 595 patient data with a diagnosis using BPJS on Symptoms of High Fever and the diagnosis is anemia, then the age used is> 60 years.\",\"PeriodicalId\":51872,\"journal\":{\"name\":\"International Journal of Information and Learning Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2022-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Information and Learning Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32938/jitu.v2i1.1036\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information and Learning Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32938/jitu.v2i1.1036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

医院是提供完整的个人保健服务的保健服务机构,提供住院服务。由于医院中大量的患者数据使管理部门难以处理现有的数据和信息,因此作者希望对医院的患者数据进行分组以产生信息。并且还可以方便医院向患者提供信息。集群是在组中寻找与其他组中的对象相同(或有关系)而与其他组中的对象不同(或不相关)的对象集合。聚类分析的目的是最小化聚类内的距离,最大化聚类之间的距离。而簇上的质心就是中心点。通过k-means算法的聚类方法进行的检验可以看出,疾病症状、诊断结果、年龄、集/值最高的组和患者诊断数据最多的标准的聚类2,即在聚类1中,共825个。患者资料与BPJS对高热症状的诊断结果一致,诊断为哮喘,年龄> 60岁。犯罪行为患者的中间数据最多,即聚类1中有595例患者数据,使用BPJS对高热症状进行诊断,诊断为贫血,则使用的年龄> 60岁。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Penerapan Data Mining Pengelompokan Hasil Diagnosa Pasien BPJS Berdasarkan Usia Menggunakan Metode Clustering (Studi Kasus: RSU Bidadari Binjai)
The hospital is a health service institution that provides complete individual health services that provide inpatient services. Because the large number of patient data in the hospital makes it difficult for the administration to process existing data and information, the authors want to group patient data at the hospital to produce information. and can also facilitate the hospital in providing information to patients. Cluster is finding a collection of objects in a group that are the same (or have a relationship) with others and different (or not related) with objects in other groups. The purpose of cluster analysis is to minimize the distance within the cluster and maximize the distance between clusters. And the Centroid on the cluster is the center point. From the tests carried out using the clustering method with the k-means algorithm, it can be seen that cluster 2 of the criteria for disease symptoms, diagnosis results, age, group that has the highest set / value and the most patient diagnosis data, namely in Cluster 1 totaling 825. Patient data with diagnosis results using BPJS on Symptoms of High Fever and the diagnosis is Asthma, then age is> 60 years. and the most intermediate data on patients who experience delinquency, namely in Cluster 1 amounted to 595 patient data with a diagnosis using BPJS on Symptoms of High Fever and the diagnosis is anemia, then the age used is> 60 years.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
International Journal of Information and Learning Technology
International Journal of Information and Learning Technology COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
6.10
自引率
3.30%
发文量
33
期刊介绍: International Journal of Information and Learning Technology (IJILT) provides a forum for the sharing of the latest theories, applications, and services related to planning, developing, managing, using, and evaluating information technologies in administrative, academic, and library computing, as well as other educational technologies. Submissions can include research: -Illustrating and critiquing educational technologies -New uses of technology in education -Issue-or results-focused case studies detailing examples of technology applications in higher education -In-depth analyses of the latest theories, applications and services in the field The journal provides wide-ranging and independent coverage of the management, use and integration of information resources and learning technologies.
期刊最新文献
Development of an Automated Hall Effect Experimentation Method for the Electrical Characterization of Thin Films Deteksi Tingkat Kematangan Buah Pinang Menggunakan Metode Support Vector Machine Berdasarkan Warna Dan Tekstur Analisis Kinerja Mikrokomputer Raspberry Pi Pada Smart Greenhouse Berbasis Internet Of Things (IoT) Menggunakan Algoritma Naive Baye SISTEM PENDUKUNG KEPUTUSAN PENENTUAN GURU BERPRESTASI MENGGUNAKAN METODE TOPSIS (STUDI KASUS: DINAS PPO KAB. TTU) Analisis Kepuasan Pengguna Terhadap Penerapan Sistem Informasi Terpadu Layanan Prodi (SIPLO) Menggunakan End User Computing Satisfaction (EUCS)
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1