Diphtheria Case Number Forecasting using Radial Basis Function Neural Network

Wiwik Anggraeni, Dina Nandika, Faizal Mahananto, Yeyen Sudiarti, Cut Alna Fadhilla
{"title":"Diphtheria Case Number Forecasting using Radial Basis Function Neural Network","authors":"Wiwik Anggraeni, Dina Nandika, Faizal Mahananto, Yeyen Sudiarti, Cut Alna Fadhilla","doi":"10.1109/ICICoS48119.2019.8982403","DOIUrl":null,"url":null,"abstract":"In Indonesia, diphtheria ranks fourth as a deadly disease after cardiovascular, tuberculosis, and pneumonia. The death rate of diphtheria is estimated to 21% with symptoms of malaise, anorexia, sore throat, and increased body temperature. The diphtheria cases which was reported in 2014 showed that East Java occupied the highest number for diphtheria cases which reached until 295, contributed to 74% cases of 22 provinces in Indonesia. In the mid-2017 until mid-2018, the Ministry of Health of the Republic of Indonesia announced that there has been an ongoing diphtheria outbreak in Indonesia. The number of diphtheria cases in East Java were highly raising up at the end of 2018. Forecasting is needed to reduce the number of diphtheria cases. The method used for forecasting is the Radial Basis Function Neural Network. Several variables are involved, including Immunization Coverage, Population Density, and Number of Cases. It is observed from the experimental results that the best model indicates only one variable involved, which is Number of Cases. This model is used to forecast the number of cases of diphtheria in Malang Regency, Surabaya City, and Sumenep Regency. The results showed that RBFNN method has a good performance for forecasting in Malang with MASE value of 0.84, Surabaya with MASE value of 0.817, and Sumenep with MASE value of 0.820, which all MASE values are less than 1.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICoS48119.2019.8982403","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In Indonesia, diphtheria ranks fourth as a deadly disease after cardiovascular, tuberculosis, and pneumonia. The death rate of diphtheria is estimated to 21% with symptoms of malaise, anorexia, sore throat, and increased body temperature. The diphtheria cases which was reported in 2014 showed that East Java occupied the highest number for diphtheria cases which reached until 295, contributed to 74% cases of 22 provinces in Indonesia. In the mid-2017 until mid-2018, the Ministry of Health of the Republic of Indonesia announced that there has been an ongoing diphtheria outbreak in Indonesia. The number of diphtheria cases in East Java were highly raising up at the end of 2018. Forecasting is needed to reduce the number of diphtheria cases. The method used for forecasting is the Radial Basis Function Neural Network. Several variables are involved, including Immunization Coverage, Population Density, and Number of Cases. It is observed from the experimental results that the best model indicates only one variable involved, which is Number of Cases. This model is used to forecast the number of cases of diphtheria in Malang Regency, Surabaya City, and Sumenep Regency. The results showed that RBFNN method has a good performance for forecasting in Malang with MASE value of 0.84, Surabaya with MASE value of 0.817, and Sumenep with MASE value of 0.820, which all MASE values are less than 1.
基于径向基函数神经网络的白喉病例数预测
在印度尼西亚,白喉是继心血管病、肺结核和肺炎之后的第四大致命疾病。白喉的死亡率估计为21%,症状为不适、厌食、喉咙痛和体温升高。2014年报告的白喉病例表明,东爪哇的白喉病例数量最多,达到295例,占印度尼西亚22个省的74%。在2017年年中至2018年年中,印度尼西亚共和国卫生部宣布,印度尼西亚发生了持续的白喉疫情。2018年底,东爪哇省白喉病例数大幅上升。需要进行预测以减少白喉病例数。用于预测的方法是径向基函数神经网络。涉及几个变量,包括免疫覆盖率、人口密度和病例数。从实验结果可以看出,最佳模型只涉及一个变量,即案例数。该模型用于预测玛琅县、泗水市和苏梅内普县的白喉病例数。结果表明,RBFNN方法在马琅、泗水和苏梅内普3个地区均具有较好的预测效果,MASE值分别为0.84、0.817和0.820,MASE值均小于1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信