Development of a machine learning model to predict the probability of health checkup participation in Japan
Enhancing health checkup participation is crucial for early detection and treatment of noncommunicable diseases and for improving public health. Effectively increasing health checkup rates requires identifying and encouraging individuals likely to adopt health-oriented behaviours. We aimed to develo...
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| Veröffentlicht in: | Public health (London) Jg. 247; S. 105889 |
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01.10.2025
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| Abstract | Enhancing health checkup participation is crucial for early detection and treatment of noncommunicable diseases and for improving public health. Effectively increasing health checkup rates requires identifying and encouraging individuals likely to adopt health-oriented behaviours. We aimed to develop a machine learning model to predict the participation probability in a specific health checkup in the following year.
Retrospective cohort study.
We analysed data from 58,863 National Health Insurance-insured individuals in Kochi Prefecture, Japan, who underwent specific health checkups during the fiscal years (FYs) 2013–2017. The dataset includes physical measurements, blood pressure measurements, blood and urine tests, and self-reported questionnaires. Predictive models for FY2018 participation were developed using LightGBM and evaluated using the area under the receiver operating characteristic curve (AUC) and reliability curves. SHAP was used to assess the feature's importance. External validation for FY2019 and FY2020 assessed temporal robustness.
Predictive accuracy for FY2018 was high, with AUCs of 0.824 (95 % confidence interval [95 % CI]: 0.813–0.835) for men and 0.820 (95 % CI: 0.810–0.830) for women. External validation of FY2019 showed AUCs of 0.821 and 0.807 for men and women, respectively. In FY2020, prediction accuracy declined, with AUCs of 0.798 and 0.794 for men and women, respectively. Key predictive features included years since the last checkup, past checkup frequency, age, systolic blood pressure, and lifestyle factors.
By developing an accurate model to predict future health checkup participation, we identified a novel indicator that enables efficient, optimized recommendations and may help improve participation rates. |
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| AbstractList | AbstractObjectivesEnhancing health checkup participation is crucial for early detection and treatment of noncommunicable diseases and for improving public health. Effectively increasing health checkup rates requires identifying and encouraging individuals likely to adopt health-oriented behaviours. We aimed to develop a machine learning model to predict the participation probability in a specific health checkup in the following year. Study designRetrospective cohort study. MethodsWe analysed data from 58,863 National Health Insurance-insured individuals in Kochi Prefecture, Japan, who underwent specific health checkups during the fiscal years (FYs) 2013–2017. The dataset includes physical measurements, blood pressure measurements, blood and urine tests, and self-reported questionnaires. Predictive models for FY2018 participation were developed using LightGBM and evaluated using the area under the receiver operating characteristic curve (AUC) and reliability curves. SHAP was used to assess the feature's importance. External validation for FY2019 and FY2020 assessed temporal robustness. ResultsPredictive accuracy for FY2018 was high, with AUCs of 0.824 (95 % confidence interval [95 % CI]: 0.813–0.835) for men and 0.820 (95 % CI: 0.810–0.830) for women. External validation of FY2019 showed AUCs of 0.821 and 0.807 for men and women, respectively. In FY2020, prediction accuracy declined, with AUCs of 0.798 and 0.794 for men and women, respectively. Key predictive features included years since the last checkup, past checkup frequency, age, systolic blood pressure, and lifestyle factors. ConclusionsBy developing an accurate model to predict future health checkup participation, we identified a novel indicator that enables efficient, optimized recommendations and may help improve participation rates. Enhancing health checkup participation is crucial for early detection and treatment of noncommunicable diseases and for improving public health. Effectively increasing health checkup rates requires identifying and encouraging individuals likely to adopt health-oriented behaviours. We aimed to develop a machine learning model to predict the participation probability in a specific health checkup in the following year.OBJECTIVESEnhancing health checkup participation is crucial for early detection and treatment of noncommunicable diseases and for improving public health. Effectively increasing health checkup rates requires identifying and encouraging individuals likely to adopt health-oriented behaviours. We aimed to develop a machine learning model to predict the participation probability in a specific health checkup in the following year.Retrospective cohort study.STUDY DESIGNRetrospective cohort study.We analysed data from 58,863 National Health Insurance-insured individuals in Kochi Prefecture, Japan, who underwent specific health checkups during the fiscal years (FYs) 2013-2017. The dataset includes physical measurements, blood pressure measurements, blood and urine tests, and self-reported questionnaires. Predictive models for FY2018 participation were developed using LightGBM and evaluated using the area under the receiver operating characteristic curve (AUC) and reliability curves. SHAP was used to assess the feature's importance. External validation for FY2019 and FY2020 assessed temporal robustness.METHODSWe analysed data from 58,863 National Health Insurance-insured individuals in Kochi Prefecture, Japan, who underwent specific health checkups during the fiscal years (FYs) 2013-2017. The dataset includes physical measurements, blood pressure measurements, blood and urine tests, and self-reported questionnaires. Predictive models for FY2018 participation were developed using LightGBM and evaluated using the area under the receiver operating characteristic curve (AUC) and reliability curves. SHAP was used to assess the feature's importance. External validation for FY2019 and FY2020 assessed temporal robustness.Predictive accuracy for FY2018 was high, with AUCs of 0.824 (95 % confidence interval [95 % CI]: 0.813-0.835) for men and 0.820 (95 % CI: 0.810-0.830) for women. External validation of FY2019 showed AUCs of 0.821 and 0.807 for men and women, respectively. In FY2020, prediction accuracy declined, with AUCs of 0.798 and 0.794 for men and women, respectively. Key predictive features included years since the last checkup, past checkup frequency, age, systolic blood pressure, and lifestyle factors.RESULTSPredictive accuracy for FY2018 was high, with AUCs of 0.824 (95 % confidence interval [95 % CI]: 0.813-0.835) for men and 0.820 (95 % CI: 0.810-0.830) for women. External validation of FY2019 showed AUCs of 0.821 and 0.807 for men and women, respectively. In FY2020, prediction accuracy declined, with AUCs of 0.798 and 0.794 for men and women, respectively. Key predictive features included years since the last checkup, past checkup frequency, age, systolic blood pressure, and lifestyle factors.By developing an accurate model to predict future health checkup participation, we identified a novel indicator that enables efficient, optimized recommendations and may help improve participation rates.CONCLUSIONSBy developing an accurate model to predict future health checkup participation, we identified a novel indicator that enables efficient, optimized recommendations and may help improve participation rates. Enhancing health checkup participation is crucial for early detection and treatment of noncommunicable diseases and for improving public health. Effectively increasing health checkup rates requires identifying and encouraging individuals likely to adopt health-oriented behaviours. We aimed to develop a machine learning model to predict the participation probability in a specific health checkup in the following year. Retrospective cohort study. We analysed data from 58,863 National Health Insurance-insured individuals in Kochi Prefecture, Japan, who underwent specific health checkups during the fiscal years (FYs) 2013–2017. The dataset includes physical measurements, blood pressure measurements, blood and urine tests, and self-reported questionnaires. Predictive models for FY2018 participation were developed using LightGBM and evaluated using the area under the receiver operating characteristic curve (AUC) and reliability curves. SHAP was used to assess the feature's importance. External validation for FY2019 and FY2020 assessed temporal robustness. Predictive accuracy for FY2018 was high, with AUCs of 0.824 (95 % confidence interval [95 % CI]: 0.813–0.835) for men and 0.820 (95 % CI: 0.810–0.830) for women. External validation of FY2019 showed AUCs of 0.821 and 0.807 for men and women, respectively. In FY2020, prediction accuracy declined, with AUCs of 0.798 and 0.794 for men and women, respectively. Key predictive features included years since the last checkup, past checkup frequency, age, systolic blood pressure, and lifestyle factors. By developing an accurate model to predict future health checkup participation, we identified a novel indicator that enables efficient, optimized recommendations and may help improve participation rates. |
| ArticleNumber | 105889 |
| Author | Oyama, Asuka Noguchi, Midori |
| Author_xml | – sequence: 1 givenname: Asuka orcidid: 0000-0002-6584-5958 surname: Oyama fullname: Oyama, Asuka organization: Health and Counseling Center, The University of Osaka, Osaka, Japan – sequence: 2 givenname: Midori orcidid: 0000-0001-9515-4077 surname: Noguchi fullname: Noguchi, Midori email: noguchi@pbhel.med.osaka-u.ac.jp organization: Division of Public Health, Department of Social Medicine, Graduate School of Medicine, The University of Osaka, Osaka, Japan |
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| Cites_doi | 10.1016/j.ypmed.2020.106301 10.1186/1471-2458-14-913 10.1016/j.bodyim.2007.06.002 10.1001/jama.2021.6524 10.1186/s12889-019-7889-4 10.4414/smw.2015.14075 10.1111/ceo.14310 10.1177/2047487318780751 10.4178/epih.e2017003 10.1001/jama.2013.5039 10.1536/ihj.49.193 10.5551/jat.64100 10.1539/joh.49.376 10.1111/phn.13110 10.1038/s42256-019-0138-9 10.1007/s40801-020-00224-5 10.1016/j.puhe.2010.04.009 10.1080/20476965.2021.1924085 10.5551/jat.42010 10.2188/jea.JE20180194 10.1214/aos/1013203451 10.1186/1471-2458-12-723 10.1016/j.cmpb.2018.05.032 |
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| Keywords | Health checkup participation rate Checkup attendance Specific health checkup Machine learning |
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| SubjectTerms | Adult Aged Checkup attendance Female Health checkup participation rate Humans Infectious Disease Internal Medicine Japan Machine Learning Male Middle Aged Physical Examination - statistics & numerical data Public Health Retrospective Studies Specific health checkup |
| Title | Development of a machine learning model to predict the probability of health checkup participation in Japan |
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