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      Tailored machine learning for evaluating the long-term diabetes risk in older individuals: findings from the Irish Longitudinal Study on Ageing (TILDA)

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          Abstract

          Objectives

          The prevalence of diabetes has increased globally, leading to a significant disease burden and financial cost. Early prediction is crucial to control its prevalence.

          Design

          A prospective cohort study.

          Setting

          National representative study on Irish.

          Participants

          8504 individuals aged 50 years or older were included.

          Primary and secondary outcome measures

          Surveys were conducted to collect over 40 000 variables related to social, financial, health, mental and family status. Feature selection was performed using logistic regression. Different machine/deep learning algorithms were trained, including distributed random forest, extremely randomised trees, a generalised linear model with regularisation, a gradient boosting machine and a deep neural network. These algorithms were integrated into a stacked ensemble to generate the best model. The model was tested using various metrics, such as the area under the curve (AUC), log loss, mean per classification error, mean square error (MSE) and root MSE (RMSE). The SHapley Additive exPlanations (SHAP) method was used to interpret the established model.

          Results

          After 2 years, 105 baseline features were identified as major contributors to diabetes risk, including sex, low-density lipoprotein cholesterol and cirrhosis. The best model achieved high accuracy, robustness and discrimination in predicting diabetes risk, with an AUC of 0.854, log loss of 0.187, mean per classification error of 0.267, RMSE of 0.229 and MSE of 0.052 in the independent test set. The model was also shown to be well calibrated. The SHAP algorithm provided insights into the decision-making process of the model.

          Conclusions

          These findings could help physicians in the early identification of high-risk patients and implement targeted interventions to reduce diabetes incidence.

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          Most cited references26

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          Global prevalence of diabetes: estimates for the year 2000 and projections for 2030.

          The goal of this study was to estimate the prevalence of diabetes and the number of people of all ages with diabetes for years 2000 and 2030. Data on diabetes prevalence by age and sex from a limited number of countries were extrapolated to all 191 World Health Organization member states and applied to United Nations' population estimates for 2000 and 2030. Urban and rural populations were considered separately for developing countries. The prevalence of diabetes for all age-groups worldwide was estimated to be 2.8% in 2000 and 4.4% in 2030. The total number of people with diabetes is projected to rise from 171 million in 2000 to 366 million in 2030. The prevalence of diabetes is higher in men than women, but there are more women with diabetes than men. The urban population in developing countries is projected to double between 2000 and 2030. The most important demographic change to diabetes prevalence across the world appears to be the increase in the proportion of people >65 years of age. These findings indicate that the "diabetes epidemic" will continue even if levels of obesity remain constant. Given the increasing prevalence of obesity, it is likely that these figures provide an underestimate of future diabetes prevalence.
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            2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2021

            (2020)
            The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
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              Extremely randomized trees

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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2023
                30 May 2023
                : 13
                : 5
                : e072991
                Affiliations
                [1 ]departmentDepartment of Endocrinology , People's Hospital of Wanning , Wanning, Hainan Province, China
                [2 ]departmentDepartment of Industrial Design , Ringgold_47896Hubei University of Technology , Wuhan, Hubei Province, China
                [3 ]departmentDepartment of Urology , Ringgold_477165The Second Affiliated Hospital of Hainan Medical University , Haikou, Hainan, China
                [4 ]departmentDepartment of Endocrinology , Ringgold_607156The First Affiliated Hospital of Hainan Medical University , Haikou, Hainan, China
                [5 ]departmentDepartment of Urology , Ringgold_607156The First Affiliated Hospital of Hainan Medical University , Haikou, Hainan, China
                Author notes
                [Correspondence to ] Dr Zhifei Che; zhifeiche@ 123456hainmc.edu.cn ; Dr. Hailong Zheng; zhlong071011@ 123456126.com
                Author information
                http://orcid.org/0000-0003-2459-0853
                Article
                bmjopen-2023-072991
                10.1136/bmjopen-2023-072991
                10255035
                37253496
                2fe6bebc-a7fe-4e20-8796-d47eca771687
                © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 21 February 2023
                : 07 May 2023
                Funding
                Funded by: Graduate Innovation research project of Hainan province;
                Award ID: Qhyb2021-57
                Funded by: FundRef http://dx.doi.org/10.13039/501100004761, Natural Science Foundation of Hainan Province;
                Award ID: 822QN472
                Categories
                Diabetes and Endocrinology
                1506
                1843
                Original research
                Custom metadata
                unlocked

                Medicine
                diabetes & endocrinology,aging
                Medicine
                diabetes & endocrinology, aging

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