{"id":1832,"date":"2024-02-15T11:07:12","date_gmt":"2024-02-15T02:07:12","guid":{"rendered":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/?p=1832"},"modified":"2025-11-11T12:32:08","modified_gmt":"2025-11-11T03:32:08","slug":"%e3%80%8cmedical-expenditure-prediction-and-regional-disparities-analysis-using-machine-learning-%e3%80%8dundergraduate-students-research-for-the-2023-academic-year","status":"publish","type":"post","link":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/en\/archives\/1832","title":{"rendered":"“Medical Expenditure Prediction and Regional Disparities Analysis Using Machine Learning\u00a0“(Undergraduate student\u2019s research for the 2023 academic year)\u00a0"},"content":{"rendered":"\n

This study aimed to predict future changes in medical expenditures and examine regional disparities using\u00a0machine\u00a0lerarning\u00a0models.<\/p>\n\n\n\n

Ministry of Health,\u00a0Labour\u00a0and Welfare\u00a0[1],\u00a0<\/a>three prefectures\u2014Akita, Niigata, and Nara\u2014were analyzed. Among several machine learning models tested, the Random Forest algorithm\u00a0demonstrated\u00a0the highest predictive accuracy. The results\u00a0indicated\u00a0a slight decrease in medical expenditures in Akita Prefecture, while expenditures were projected to increase in Niigata and Nara Prefectures. Akita showed progress in home-based medical care; Niigata\u00a0exhibited\u00a0slower development in healthcare infrastructure; and Nara, despite having many medical facilities, had a\u00a0relatively low\u00a0rate of health checkup participation.\u00a0<\/p>\n\n\n\n

These findings highlight the importance of promoting region-specific healthcare initiatives tailored to local characteristics.\u00a0\u00a0<\/p>\n\n\n\n

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[1] Ministry of Health,\u00a0Labour\u00a0and Welfare: National Health Expenditures: Summary of Results, https:\/\/www.mhlw.go.jp\/toukei\/list\/37-21c.html, (Accessed 2023-06-05).\u00a0<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

This study aimed to predict future changes in medical expenditures and examine regional disparities using machine lerarning models.<\/p>\n","protected":false},"author":14,"featured_media":1825,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_lmt_disableupdate":"no","_lmt_disable":"","_locale":"en_US","_original_post":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/?p=1824","footnotes":""},"categories":[24],"tags":[],"class_list":["post-1832","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-24","en-US"],"modified_by":"\u89d2\u7530\u3000\u7a1a\u5b99","_links":{"self":[{"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/posts\/1832","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/comments?post=1832"}],"version-history":[{"count":2,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/posts\/1832\/revisions"}],"predecessor-version":[{"id":1837,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/posts\/1832\/revisions\/1837"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/media\/1825"}],"wp:attachment":[{"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/media?parent=1832"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/categories?post=1832"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.comm.tcu.ac.jp\/masuda-lab\/wp-json\/wp\/v2\/tags?post=1832"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}