Hostname: page-component-54dcc4c588-trf7k Total loading time: 0 Render date: 2025-10-13T14:40:20.642Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicted Number of Pregnant Women in Aichi Prefecture, Japan: Estimation by Machine Learning Database Construction for Disaster Preparation

Published online by Cambridge University Press:  04 June 2021

Kanetoshi Hattori  and
Ritsuko Hattori
Kanetoshi Hattori*
Affiliation:
Faculty of Health Science, Naragakuen University, Nara, Japan
Ritsuko Hattori
Affiliation:
Faculty of Health Science, Naragakuen University, Nara, Japan
*
Corresponding author: Kanetoshi Hattori, Email: hattorik@mx6.mesh.ne.jp.
Get access Rights & Permissions [Opens in a new window]

Abstract

Aichi prefecture, Japan is predicted to be hit by Mega-earthquake. Aichi Prefectural Association of Midwives has been making efforts to improve disaster preparedness for pregnant women. This project aims to acquire area data of pregnant women for simulated studies of rescue activities. Number of women in census survey areas in Nagoya City was acquired from nationwide data of pregnant women by machine learning (Cascade-Correlation Learning Architecture). Quite high correlation coefficients between actual data and estimation data were observed. Rescue simulations have been carried out based on the data acquired by this study.

Keywords

pregnant womenmachine learningdisaster planningmidwifeCascade-Correlation Learning Architecture

Information

Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 16 , Issue 3 , June 2022 , pp. 940 - 948
Copyright
© Society for Disaster Medicine and Public Health, Inc. 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

City of Nagoya. On Tokai Trough mega-earthquake. 2005. http://www.city.nagoya.jp/bosaikikikanri/page/0000003746.html. Text in Japanese. Accessed February 29, 2020.Google Scholar
Aichi Prefecture Disaster Defense Earthquake Committee. Results of damage prediction survey of Tokai earthquake, Tonankai earthquake and Nankai earthquake of Aichi Prefecture. 2014. http://www.pref.aichi.jp/bousai/2014higaiyosoku/whitebooknew2.pdf. Text in Japanese (GIS mesh data were provided by the prefecture by digital media). Accessed July 1, 2019.Google Scholar
Aichi Prefectural Midwives Association. 2019 Project Master Plan of Aichi Prefectural Midwives Association (author). 2016. http://www1.s3.starcat.ne.jp/aijo/28keikaku.pdf. Text in Japanese. Accessed May 23, 2020.Google Scholar
Aichi Nursing Association. Disaster support nurse. 2019. https://www.aichi-kangokyokai.or.jp/publics/index/37/. Text in Japanese. Accessed May 17, 2020.Google Scholar
Japanese Midwives Association. Pearls of midwife’s wisdom (“Chiebukuro”) for disaster: disaster preparedness guidebook for pregnant women and mothers with children. 2016. http://www.midwife.or.jp/pdf/chiebukuro/chiebukuro_280420.pdf. Text in Japanese. Accessed May 23, 2020.Google Scholar
Sugawara, J. Information-sharing manual for pregnant women in disaster, for health and medical professionals. Tohoku Medical Megabank Organization. 2016. http://www.megabank.tohoku.ac.jp/wp/wp-content/uploads/2016/04/ID14985_0202.pdf. Text in Japanese. Accessed September 16, 2020.Google Scholar
Sugawara, J. Information-sharing manual for pregnant women in disaster, for general public and evacuation center staff. Tohoku Medical Megabank Organization. 2016. http://www.megabank.tohoku.ac.jp/wp/wp-content/uploads/2016/04/ID14985_01.pdf. Text in Japanese. Accessed September 16, 2020.Google Scholar
Ministry of Health, Labor and Welfare. Survey of local healthcare centers and their executive agencies. 2018. https://www.mhlw.go.jp/file/06-Seisakujouhou-10900000-Kenkoukyoku/0000203248.pdf#search=%27%E8%A8%AD%E7%BD%AE%E4%B8%BB%E4%BD%93%E5%88%A5%E4%BF%9D%E5%81%A5%E6%89%80%E6%95%B0%27. Text in Japanese. Accessed March 10, 2020.Google Scholar
Fahlman, S, Lebiere, C. The Cascade-Correlation Learning Architecture. 1991. https://www.researchgate.net/publication/2427110_The_Cascade-Correlation_Learning_Architecture. Accessed March 3, 2020.Google Scholar
Ministry of Health, Labor and Welfare. Number of registered pregnancy at city, ward, town and village. Report of Regional Health and Health Promotion Operations. 2019. https://www.e-stat.go.jp/stat-search/files?page=1&layout=datalist&toukei=00450025&tstat=000001030884&cycle=8&tclass1=000001126815&tclass2=000001126816&tclass3=000001126819. Text in Japanese. Accessed June 30, 2019.Google Scholar
Ministry of Internal Affairs and Communication. Population by age group of city, ward, town and village, survey of population, population dynamics, and number of households based on basic resident registry. 2017. https://www.e-stat.go.jp/stat-search/files?page=1&layout=datalist&toukei=00200241&tstat=000001039591&cycle=7&year=20170&month=0&tclass1=000001039601&result_back=1. Text in Japanese. Accessed June 30, 2019.Google Scholar
Big Data. Sensitivity analysis. May 29, 2017. http://www.big-data.tips/sensitivity-analysis. Accessed March 2, 2020.Google Scholar
Ministry of Internal Affairs and Communication. 2015 National census population of Aichi Prefecture (5 year age grouped by sex, small community areas). 2017. https://www.e-stat.go.jp/gis/statmap-search?page=2&type=1&toukeiCode=00200521&toukeiYear=2015&aggregateUnit=A&serveyId=A002005212015&statsId=T000849. Text in Japanese. Accessed July 12, 2019.Google Scholar
Ministry of Health, Labor and Welfare. Number of registered pregnancy at city, ward, town and village. Report of Regional Health and Health Promotion Operations. 2017. https://www.e-stat.go.jp/stat-search/files?page=1&layout=datalist&toukei=00450025&tstat=000001030884&cycle=8&tclass1=000001096975&tclass2=000001096982&stat_infid=000031548985. Text in Japanese. Accessed June 30, 2019.Google Scholar
QGIS Consortium. QGIS Version 3.8.1 Zanzibar, author. 2019. https://www.qgis.org/ja/site/index.html. Accessed August 14, 2019.Google Scholar
Statistics Bureau of Japan. 2015 National Census of Japan. 2017. https://www.e-stat.go.jp/stat-search/files?page=1&layout=datalist&toukei=00200521&tstat=000001049104&cycle=0&tclass1=000001049105. Accessed February 29, 2020.Google Scholar
Ministry of Land, Infrastructure, Transport and Tourism. National land numerical information. 2020. https://nlftp.mlit.go.jp/ksj/index.html. Text in Japanese. Accessed May 24, 2020.Google Scholar
Hattori, R, Miyagawa, S, Hattori, K. Estimation of pregnant women possibly left behind in tsunami predicted areas of Nagoya City: a simulation for rescue planning. Disaster Med Public Health Prep. 2020;epub, 1-8.Google ScholarPubMed
Hattori, K, Hattori, R, Taneichi, H, et al. Road centerline data accumulation for rescue workers whose expertise is not GIS. Disaster Med Public Health Prep. 2020;epub, 1-7.Google Scholar
Minsky, ML, Papert, SA. Perceptrons: an introduction to computational geometry. Massachusetts Institute of Technology; 1988 (expanded edition). https://leon.bottou.org/publications/pdf/perceptrons-2017.pdf. Accessed March 8, 2020.Google Scholar
Sakurai, A, Ootake, K. Talk: changes of machine learning and prospect towards future. J Jpn Soc Data Sci. 2018;2(1):2-9.Google Scholar
Castro S. Machine learning to the rescue! 2018. https://blogs.mathworks.com/racing-lounge/2018/07/04/machine-learning-to-the-rescue/ Accessed March 8, 2020.Google Scholar
Hashemeipour, M, Studan, SM, Dever, JR. A community-based disaster coordination framework for effective disaster preparedness and response. Aust J Emerg Manag. 2017;32(2):41-46.Google Scholar
Nabian, MA, Meidani, H. Accelerating Stochastic Assessment of Post-Earthquake Transportation Network Connectivity via Machine-Learning-Based Surrogates, Transportation Research Board 97th Annual Meeting. 2018. https://www.researchgate.net/publication/326208944_Accelerating_Stochastic_Assessment_of_Post-Earthquake_Transportation_Network_Connectivity_Via_Machine-Learning-Based_SurrogatesGoogle Scholar
Chang, LC, Amin, MZM, Yang, SN, Chang, FJ. Building ANN-based regional multi-step-ahead flood inundation forecast models. Water. 2018;10:206-225. https://books.google.co.jp/books?id=mFeNDwAAQBAJ&pg=PA209&lpg=PA209&dq=Disaster+management+in+real+time+simulation+using+machine+learning&source=bl&ots=9pkOf6c9PG&sig=ACfU3U0m90Hj_OcwWJV_zmNh-B3oN22_-w&hl=ja&sa=X&ved=2ahUKEwiy6uW-0IfoAhXL7WEKHYoDABcQ6AEwBXoECAoQAQ#v=onepage&q&f=false.CrossRefGoogle Scholar
Ouchi, J, Hattori, K. Estimated number of short stay service recipients in Hokkaido Prefecture, Japan from 2020 to 2045: estimation by machine learning and review of changing trend by cartogram. Nurs Res. 2020;epub, doi: 10.21203/rs.3.rs-111572/v1.Google Scholar