Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults
Identifying people with a high risk of developing diabetes among those with prediabetes may facilitate the implementation of a targeted lifestyle and pharmacological interventions. We aimed to establish machine learning models based on demographic and clinical characteristics to predict the risk of...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/12/7/1055 |
| _version_ | 1797445982666883072 |
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| author | Qing Liu Qing Zhou Yifeng He Jingui Zou Yan Guo Yaqiong Yan |
| author_facet | Qing Liu Qing Zhou Yifeng He Jingui Zou Yan Guo Yaqiong Yan |
| author_sort | Qing Liu |
| collection | DOAJ |
| description | Identifying people with a high risk of developing diabetes among those with prediabetes may facilitate the implementation of a targeted lifestyle and pharmacological interventions. We aimed to establish machine learning models based on demographic and clinical characteristics to predict the risk of incident diabetes. We used data from the free medical examination service project for elderly people who were 65 years or older to develop logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) machine learning models for the follow-up results of 2019 and 2020 and performed internal validation. The receiver operating characteristic (ROC), sensitivity, specificity, accuracy, and F1 score were used to select the model with better performance. The average annual progression rate to diabetes in prediabetic elderly people was 14.21%. Each model was trained using eight features and one outcome variable from 9607 prediabetic individuals, and the performance of the models was assessed in 2402 prediabetes patients. The predictive ability of four models in the first year was better than in the second year. The XGBoost model performed relatively efficiently (ROC: 0.6742 for 2019 and 0.6707 for 2020). We established and compared four machine learning models to predict the risk of progression from prediabetes to diabetes. Although there was little difference in the performance of the four models, the XGBoost model had a relatively good ROC value, which might perform well in future exploration in this field. |
| first_indexed | 2024-03-09T13:33:55Z |
| format | Article |
| id | doaj.art-f259ecf57e1f408c9d5f4de16d352119 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-09T13:33:55Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-f259ecf57e1f408c9d5f4de16d3521192023-11-30T21:14:52ZengMDPI AGJournal of Personalized Medicine2075-44262022-06-01127105510.3390/jpm12071055Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly AdultsQing Liu0Qing Zhou1Yifeng He2Jingui Zou3Yan Guo4Yaqiong Yan5Department of Epidemiology, School of Public Health, Wuhan University, Wuhan 430071, ChinaDepartment of Epidemiology, School of Public Health, Wuhan University, Wuhan 430071, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaWuhan Center for Disease Control and Prevention, Wuhan 430015, ChinaWuhan Center for Disease Control and Prevention, Wuhan 430015, ChinaIdentifying people with a high risk of developing diabetes among those with prediabetes may facilitate the implementation of a targeted lifestyle and pharmacological interventions. We aimed to establish machine learning models based on demographic and clinical characteristics to predict the risk of incident diabetes. We used data from the free medical examination service project for elderly people who were 65 years or older to develop logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) machine learning models for the follow-up results of 2019 and 2020 and performed internal validation. The receiver operating characteristic (ROC), sensitivity, specificity, accuracy, and F1 score were used to select the model with better performance. The average annual progression rate to diabetes in prediabetic elderly people was 14.21%. Each model was trained using eight features and one outcome variable from 9607 prediabetic individuals, and the performance of the models was assessed in 2402 prediabetes patients. The predictive ability of four models in the first year was better than in the second year. The XGBoost model performed relatively efficiently (ROC: 0.6742 for 2019 and 0.6707 for 2020). We established and compared four machine learning models to predict the risk of progression from prediabetes to diabetes. Although there was little difference in the performance of the four models, the XGBoost model had a relatively good ROC value, which might perform well in future exploration in this field.https://www.mdpi.com/2075-4426/12/7/1055machine learningprediabetesincident diabetespredictive models |
| spellingShingle | Qing Liu Qing Zhou Yifeng He Jingui Zou Yan Guo Yaqiong Yan Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults Journal of Personalized Medicine machine learning prediabetes incident diabetes predictive models |
| title | Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults |
| title_full | Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults |
| title_fullStr | Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults |
| title_full_unstemmed | Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults |
| title_short | Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults |
| title_sort | predicting the 2 year risk of progression from prediabetes to diabetes using machine learning among chinese elderly adults |
| topic | machine learning prediabetes incident diabetes predictive models |
| url | https://www.mdpi.com/2075-4426/12/7/1055 |
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