Establishment of effective strategy for classification of individuals who need to receive health guidance using data mining technique

Research Project

Project/Area Number	16K09062
Research Category	Grant-in-Aid for Scientific Research (C)
Allocation Type	Multi-year Fund
Section	一般
Research Field	Epidemiology and preventive medicine
Research Institution	Sapporo Medical University
Principal Investigator	Ohnishi Hirofumi 札幌医科大学, 医学部, 教授 (20359996)
Co-Investigator(Kenkyū-buntansha)	斉藤重幸札幌医科大学, 保健医療学部, 教授 (60253994) 赤坂憲大阪大学, 医学部附属病院, 助教 (70468081)
Project Period (FY)	2016-04-01 – 2020-03-31
Project Status	Completed (Fiscal Year 2019)
Budget Amount *help	¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000) Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000) Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000) Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Keywords	特定健診・特定保健指導 / 保健事業 / 生活習慣病罹患ハイリスク者階層化 / データマイニング / 地域一般住民コホート / 生活習慣病ハイリスク者階層化 / 社会医学 / 疫学・予防医学 / 特定健診
Outline of Final Research Achievements	We attempted to effectively stratify groups of health checkup examinees for lifestyle-related disease prevention by applying a decision tree analysis and a cluster analysis in our community-based cohort study. We were able to classify health checkup examinee into several groups with similar characteristics of the subject such as risk for future occurrence of hypertension and type 2 diabetes from the results of longitudinal analyses and one’s current lifestyle including diet and demand for health service. It was suggested that health workers in local governments could implement effective lifestyle-related disease prevention with less human resource when using these classification methods.
Academic Significance and Societal Importance of the Research Achievements	市町村国保がデータヘルス計画の中の保健事業を立案する上で、被保険者集団を疾病罹患リスクや、年代や生活スタイル、生活習慣改善意欲などに基づいてターゲットを絞ることは重要であるが、どのような方法で効果的に対象を分類・階層化するかの方法論はまだ確立していない。本研究によって、過去に蓄積された健診データをデータマイニングの手法を用いて分析することで、受診者の健診結果に基づき将来の疾病罹患リスクや対象特性の近い集団に効果的に分類・階層化し、市町村の保健従事者の限られた人的・経済的資源においても効果的な保健事業が運営できる可能性が示唆された。