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In some countries, including Japan—the leading country in terms of longevity, life expectancy has been increasing; meanwhile, healthy life years have not kept pace, necessitating an effective health policy to narrow the gap.
The aim of this study is to develop a prediction model for healthy life years without activity limitations and deploy the model in a health policy to prolong healthy life years.
The Comprehensive Survey of Living Conditions, a cross-sectional national survey of Japan, was conducted by the Japanese Ministry of Health, Labour and Welfare in 2013, 2016, and 2019. The data from 1,537,773 responders were used for modelling using machine learning. All participants were randomly split into training (n=1,383,995, 90%,) and test (n=153,778, 10%) subsets. Extreme gradient boosting classifier was implemented. Activity limitations were set as the target. Age, sex, and 40 types of diseases or injuries were included as features. Healthy life years without activity limitations were calculated by incorporating the predicted prevalence rate of activity limitations in a life table. For the wide utility of the model in individuals, we developed an application tool for the model.
In the groups without (n=1,329,901) and with (n=207,872) activity limitations, the median age was 47 (IQR 30-64) and 69 (IQR 54-80) years, respectively (
The prediction model will enable national or regional governments to establish an effective health promotion policy for risk prevention at the population and individual levels to prolong healthy life years. Further investigation is needed to validate the model’s adaptability to various ethnicities and, in particular, to countries where the population exhibits a short life span.
Global public health, secure social systems, and advances in medical practice have contributed to the extension of life expectancy and healthy life years (referred to as the healthy life expectancy) of humans worldwide. With the growing recognition of the importance of taking into account the state of being alive or quality of life, “healthy life years” has come to be focused on as an integrated health indicator that combines not only mortality data but also data on the state of being alive. Healthy life years are not merely defined as life without disability or illness but include a holistic life of well-being. Although life expectancy has been increasing, healthy life years have not yet been kept pace, necessitating an effective health policy to narrow the gap [
There have been several measures to estimate healthy life years, which are used to evaluate national or regional health status. The World Health Organization has used the health-adjusted life expectancy, a measure of healthy life years based on a specialized health survey producing disability weight on various diseases, injuries, and sequelae [
To date, some determinants of healthy life years and the relevant activity limitations have been identified. Typical risk factors, such as obesity, hypertension, hyperglycemia, smoking, and excessive alcohol consumption, are negatively associated with a healthy life [
Despite the increasing interest in a healthy life for public health campaign and individual health awareness, a prediction model of healthy life years with integrated features has not been reported. In this study, we sought to develop a prediction model for healthy life years without activity limitations using machine learning and to deploy the model to a health policy in prolonging healthy life years at the population and individual levels.
The Comprehensive Survey of Living Conditions, a cross-sectional national survey, is conducted every 3 years by the Japanese Ministry of Health, Labour and Welfare to investigate the fundamental aspects of the nation’s livelihood, such as health, medical care, welfare, pension, and income [
The activity limitations, which were classified as binary, were set as model target; the “activity limitations” group was classified as 1, and the “no activity limitation” group as 0. Age, sex, and the 40 types of diseases or injuries under treatment were included as features. We implemented the extreme gradient boosting (XGB) classifier—a widely used supervised tree-based model, which uses labeled data sets to train a model [
The impact of the features on the model accuracy was estimated by permutation importance, which is defined as difference of error when a feature value is randomly shuffled, assigning 1.0 to the highest impact. The SHapley Additive exPlanations (SHAP) value, which explains a feature contribution on model output in each sample, was used to evaluate the effect of features on the model output [
General descriptive statistics were performed in R (version 4.2.0; R Core Team) [
The study was approved by the ethics committee of Kyoto Prefectural University of Medicine (approval number ERB-C-2496). This study conformed to the principles outlined in the Declaration of Helsinki. Since this study used only existing national survey data, opt-out decline was adopted for participants on the university website instead of informed consent. The study data are anonymous. There was no compensation for participants.
The characteristics of participants (N=1,537,773) are described and stratified according to the presence of activity limitations (
Characteristics of participants stratified by presence of activity limitations.
Characteristics | All (N=1,537,773) | Without activity limitation (n=1,329,901) | With activity limitation (n=207,872) | |
Age (years), median (IQR) | 50 (32-67) | 47 (30-64) | 69 (54-80) | <.001 |
Sex (female), n (%) | 800,133 (52) | 681,794 (51.3) | 118,339 (56.9) | <.001 |
Diabetes, n (%) | 77,672 (5.1) | 53,671 (4) | 24,001 (11.5) | <.001 |
Thyroid disease, n (%) | 19,811 (1.3) | 14,360 (1.1) | 5451 (2.6) | <.001 |
Depression or other mental disease, n (%) | 29,074 (1.9) | 13,727 (1) | 15,347 (7.4) | <.001 |
Dementia, n (%) | 11,087 (0.7) | 2420 (0.2) | 8667 (4.2) | <.001 |
Parkinson disease, n (%) | 3194 (0.2) | 533 (0) | 2661 (1.3) | <.001 |
Other neurological disorders, pain, or paralysis, n (%) | 11,028 (0.7) | 4311 (0.3) | 6717 (3.2) | <.001 |
Eye disease, n (%) | 83,577 (5.4) | 52,941 (4) | 30,636 (14.7) | <.001 |
Ear disease, n (%) | 16,411 (1.1) | 9354 (0.7) | 7057 (3.4) | <.001 |
Stroke, cerebral hemorrhage, or infarction, n (%) | 19,270 (1.3) | 8452 (0.6) | 10,818 (5.2) | <.001 |
Angina and myocardial infarction, n (%) | 29,510 (1.9) | 16,467 (1.2) | 13,043 (6.3) | <.001 |
Other cardiovascular disease, n (%) | 28,703 (1.9) | 15,653 (1.2) | 13,050 (6.3) | <.001 |
Acute nasopharyngitis and common cold, n (%) | 5125 (0.3) | 3549 (0.3) | 1576 (0.8) | <.001 |
Infertility, n (%) | 1536 (0.1) | 1359 (0.1) | 177 (0.1) | .02 |
Dental disease, n (%) | 80,560 (5.2) | 63,668 (4.8) | 16,892 (8.1) | <.001 |
Gout, n (%) | 15,396 (1) | 12,090 (0.9) | 3306 (1.6) | <.001 |
Obesity, n (%) | 8038 (0.5) | 5013 (0.4) | 3025 (1.5) | <.001 |
Dyslipidemia, n (%) | 81,338 (5.3) | 63,404 (4.8) | 17,934 (8.6) | <.001 |
Hypertension, n (%) | 206,103 (13.4) | 153,500 (11.5) | 52,603 (25.3) | <.001 |
Allergic rhinitis, n (%) | 32,310 (2.1) | 24,472 (1.8) | 7838 (3.8) | <.001 |
Chronic obstructive pulmonary disease, n (%) | 2250 (0.1) | 813 (0.1) | 1437 (0.7) | <.001 |
Asthma, n (%) | 19,022 (1.2) | 13,149 (1) | 5873 (2.8) | <.001 |
Other respiratory disease, n (%) | 15,134 (1) | 8517 (0.6) | 6617 (3.2) | <.001 |
Stomach or duodenum disease, n (%) | 26,285 (1.7) | 17,048 (1.3) | 9237 (4.4) | <.001 |
Liver or gallbladder disease, n (%) | 14,624 (1) | 9283 (0.7) | 5341 (2.6) | <.001 |
Other digestive disease, n (%) | 18,656 (1.2) | 11,028 (0.8) | 7628 (3.7) | <.001 |
Atopic dermatitis, n (%) | 14,353 (0.9) | 11,553 (0.9) | 2800 (1.3) | <.001 |
Other skin disease, n (%) | 29,205 (1.9) | 20,475 (1.5) | 8730 (4.2) | <.001 |
Rheumatoid arthritis, n (%) | 11,392 (0.7) | 5153 (0.4) | 6239 (3) | <.001 |
Arthritis, n (%) | 35,435 (2.3) | 15,682 (1.2) | 19,753 (9.5) | <.001 |
Stiff shoulder, n (%) | 43,474 (2.8) | 28,093 (2.1) | 15,381 (7.4) | <.001 |
Back pain, n (%) | 80,836 (5.3) | 42,856 (3.2) | 37,980 (18.3) | <.001 |
Osteoporosis, n (%) | 28,790 (1.9) | 14,606 (1.1) | 14,184 (6.8) | <.001 |
Kidney disease, n (%) | 15,281 (1) | 7415 (0.6) | 7866 (3.8) | <.001 |
Prostatic hypertrophy, n (%) | 19,932 (1.3) | 12,293 (0.9) | 7639 (3.7) | <.001 |
Menopausal or postmenopausal disorder, n (%) | 3041 (0.2) | 1980 (0.1) | 1061 (0.5) | <.001 |
Bone fracture, n (%) | 10,464 (0.7) | 3345 (0.3) | 7119 (3.4) | <.001 |
Other injury or burns, n (%) | 10,230 (0.7) | 5451 (0.4) | 4779 (2.3) | <.001 |
Anemia or blood disease, n (%) | 10,660 (0.7) | 5980 (0.4) | 4680 (2.3) | <.001 |
Malignant neoplasm or cancer, n (%) | 13,843 (0.9) | 7594 (0.6) | 6249 (3) | <.001 |
Pregnancy, puerperium, threatened abortion, or placenta previa, n (%) | 2198 (0.1) | 1565 (0.1) | 633 (0.3) | <.001 |
To create the model feature set, the AUROC was compared for each feature number. A total of 42 features were included in the feature set because they had the highest AUROC (Figure S1 in
The accuracy metrics were compared for some learners. We selected the XGB classifier as a learner because it exhibited a high AUORC and low log loss compared with random forest and logistic regression (Table S2 in
Feature impact estimated by permutation importance. Permutation importance was calculated for features using test data.
Evaluation of model performance. (A) Receiver operating characteristic curve for the model. Area under the receiver operating characteristic curve was 0.846 (95% CI 0.842-0.849). (B) Calibration plot for the model; samples were divided into 10 bins according to probability. (C) Mean cost-benefit curve. (D) Predictive and observed value of healthy life years for male and female respondents in each year.
For model application at the population level, we used the prediction model for a regional health policy regarding healthy life years (Table S3 in
To enhance the interpretability of the feature effect on the model output, the SHAP value is displayed for each feature (
Feature effect on model output. SHapley Additive exPlanations (SHAP) value was calculated for features using test data.
Health condition without activity limitation (HCAL) per age. The HCAL index indicates subtraction of the percentage of predictive probability from 100. Curve fitting was done using third-order polynomial regression. Error bar indicates 95% CI. Each dot represents mean HCAL index per age.
In this study, we developed a prediction model for healthy life years without activity limitations using machine learning by analyzing a cross-sectional national survey. The model exhibited markedly high performance with a high AUROC and subtle differences between the observed and predicted values of healthy life years without activity limitations. We applied the prediction model to a regional health policy to prolong healthy life years by adjusting the representative predictors to a target prevalence rate. Additionally, we presented the HCAL index, followed by the application development, for individual health promotion.
We estimated the feature impact on model accuracy by permutation importance and the effect on model output by the SHAP value. The impact of features on model accuracy showed that age had the highest impact, followed by depression or other mental disease; back pain; bone fracture; other neurological disorders, pain, or paralysis; stroke, cerebral hemorrhage, or infarction; arthritis; Parkinson disease; dementia; other injuries or burns. Interestingly, the high-impact features included several nonfatal conditions, such as mental disorders, musculoskeletal problems, and neurological diseases. Our findings were consistent with previous reports that suggest mental health disorders and musculoskeletal problems are crucial predictive factors for activity limitations [
We leveraged machine learning to predict healthy life years without activity limitations. The presence of activity limitations assessed by a subjective questionnaire was used for the model target; nonetheless, healthy life years could be predicted accurately with the objective 42 features using machine learning. Machine learning facilitated model deployment by application development at the population and individual levels. Natural language processing has been applied to calculate the health-adjusted life expectancy using electronic medical records [
We demonstrated the model application for population and individual health. Healthy life years without activity limitations of females in Kyoto prefecture were simulated using the prediction model with the original and target prevalence rates of representative predictors. Thus, the model could be used to present effective ways to prolong healthy life years for a regional health policy. Moreover, the application tool was developed using the model for wide utility in individual health promotion. The tool can be used in several situations, such as health check, patient education, and outpatient clinics. Our model was developed with machine learning and can be used for prediction of population-level healthy life years as well as individual health conditions, increasing its feasibility compared with other measures for healthy life years.
This study had certain limitations, as it was based on a survey that included subjective data, and only data from Japan were used. Further investigation is needed to validate the model’s adaptability to various ethnicities and, in particular, countries where the population has a short life span. For complexity of machine learning to explain and interpret, we used permutation importance and SHAP values for feature impact.
In conclusion, we developed a prediction model for healthy life years without activity limitations, using machine learning. The prediction model will enable the national or regional government to establish an effective health promotion policy for risk prevention at the population and individual levels to prolong healthy life years. It would be interesting to investigate the model’s applicability to other countries and ethnicities.
Supplementary materials.
area under the receiver operating characteristic curve
health condition without activity limitation
SHapley Additive exPlanations
extreme gradient boosting
We are grateful to Tomoyuki Yamamoto, Mika Yamashita, and Kumiko Katsuyama from the Department of Health and Welfare, Kyoto Prefectural Government, Japan.
This study was supported by Foundation for Total Health Promotion.
The data set generated and analyzed during this study is not publicly available due to restrictions imposed by Japanese Ministry of Health, Labour and Welfare (the data provider) but can be obtained by contacting the ministry based on reasonable request.
MN was responsible for conception of the study. Formal analysis was performed by MN and RN. All authors participated in manuscript writing and approved the final manuscript. SM provided overall supervision. All the authors were responsible for the decision to submit the manuscript for publication.
None declared.