Explainable artificial intelligence on life satisfaction, diabetes mellitus and its comorbid condition
Abstract This study uses artificial intelligence for testing (1) whether the comorbidity of diabetes and its comorbid condition is very strong in the middle-aged or old (hypothesis 1) and (2) whether major determinants of the comorbidity are similar for different pairs of diabetes and its comorbid c...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-36285-z |
| _version_ | 1827895163459993600 |
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| author | Ranyeong Kim Chae-Won Kim Hyuntae Park Kwang-Sig Lee |
| author_facet | Ranyeong Kim Chae-Won Kim Hyuntae Park Kwang-Sig Lee |
| author_sort | Ranyeong Kim |
| collection | DOAJ |
| description | Abstract This study uses artificial intelligence for testing (1) whether the comorbidity of diabetes and its comorbid condition is very strong in the middle-aged or old (hypothesis 1) and (2) whether major determinants of the comorbidity are similar for different pairs of diabetes and its comorbid condition (hypothesis 2). Three pairs are considered, diabetes-cancer, diabetes-heart disease and diabetes-mental disease. Data came from the Korean Longitudinal Study of Ageing (2016–2018), with 5527 participants aged 56 or more. The evaluation of the hypotheses were based on (1) whether diabetes and its comorbid condition in 2016 were top-5 determinants of the comorbidity in 2018 (hypothesis 1) and (2) whether top-10 determinants of the comorbidity in 2018 were similar for different pairs of diabetes and its comorbid condition (hypothesis 2). Based on random forest variable importance, diabetes and its comorbid condition in 2016 were top-2 determinants of the comorbidity in 2018. Top-10 determinants of the comorbidity in 2018 were the same for different pairs of diabetes and its comorbid condition: body mass index, income, age, life satisfaction—health, life satisfaction—economic, life satisfaction—overall, subjective health and children alive in 2016. In terms of SHAP values, the probability of the comorbidity is expected to decrease by 0.02–0.03 in case life satisfaction overall is included to the model. This study supports the two hypotheses, highlighting the importance of preventive measures for body mass index, socioeconomic status, life satisfaction and family support to manage diabetes and its comorbid condition. |
| first_indexed | 2024-03-12T22:17:36Z |
| format | Article |
| id | doaj.art-2a99b13c6c4c4f66b705d19a9b6804b9 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-12T22:17:36Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-2a99b13c6c4c4f66b705d19a9b6804b92023-07-23T11:12:20ZengNature PortfolioScientific Reports2045-23222023-07-0113111010.1038/s41598-023-36285-zExplainable artificial intelligence on life satisfaction, diabetes mellitus and its comorbid conditionRanyeong Kim0Chae-Won Kim1Hyuntae Park2Kwang-Sig Lee3Department of Public Health Sciences, Graduate School of Korea UniversityAI Center, Korea University College of MedicineDepartment of Obstetrics and Gynecology, Korea University College of MedicineAI Center, Korea University College of MedicineAbstract This study uses artificial intelligence for testing (1) whether the comorbidity of diabetes and its comorbid condition is very strong in the middle-aged or old (hypothesis 1) and (2) whether major determinants of the comorbidity are similar for different pairs of diabetes and its comorbid condition (hypothesis 2). Three pairs are considered, diabetes-cancer, diabetes-heart disease and diabetes-mental disease. Data came from the Korean Longitudinal Study of Ageing (2016–2018), with 5527 participants aged 56 or more. The evaluation of the hypotheses were based on (1) whether diabetes and its comorbid condition in 2016 were top-5 determinants of the comorbidity in 2018 (hypothesis 1) and (2) whether top-10 determinants of the comorbidity in 2018 were similar for different pairs of diabetes and its comorbid condition (hypothesis 2). Based on random forest variable importance, diabetes and its comorbid condition in 2016 were top-2 determinants of the comorbidity in 2018. Top-10 determinants of the comorbidity in 2018 were the same for different pairs of diabetes and its comorbid condition: body mass index, income, age, life satisfaction—health, life satisfaction—economic, life satisfaction—overall, subjective health and children alive in 2016. In terms of SHAP values, the probability of the comorbidity is expected to decrease by 0.02–0.03 in case life satisfaction overall is included to the model. This study supports the two hypotheses, highlighting the importance of preventive measures for body mass index, socioeconomic status, life satisfaction and family support to manage diabetes and its comorbid condition.https://doi.org/10.1038/s41598-023-36285-z |
| spellingShingle | Ranyeong Kim Chae-Won Kim Hyuntae Park Kwang-Sig Lee Explainable artificial intelligence on life satisfaction, diabetes mellitus and its comorbid condition Scientific Reports |
| title | Explainable artificial intelligence on life satisfaction, diabetes mellitus and its comorbid condition |
| title_full | Explainable artificial intelligence on life satisfaction, diabetes mellitus and its comorbid condition |
| title_fullStr | Explainable artificial intelligence on life satisfaction, diabetes mellitus and its comorbid condition |
| title_full_unstemmed | Explainable artificial intelligence on life satisfaction, diabetes mellitus and its comorbid condition |
| title_short | Explainable artificial intelligence on life satisfaction, diabetes mellitus and its comorbid condition |
| title_sort | explainable artificial intelligence on life satisfaction diabetes mellitus and its comorbid condition |
| url | https://doi.org/10.1038/s41598-023-36285-z |
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