Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China
Background: Patients with diabetes are more likely to be predisposed to fractures compared to those without diabetes. In clinical practice, predicting fracture risk in diabetics is still difficult because of the limited availability and accessibility of existing fracture prediction tools in the diab...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402305394X |
| _version_ | 1797771408591290368 |
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| author | Sijia Chu Aijun Jiang Lyuzhou Chen Xi Zhang Xiurong Shen Wan Zhou Shandong Ye Chao Chen Shilu Zhang Li Zhang Yang Chen Ya Miao Wei Wang |
| author_facet | Sijia Chu Aijun Jiang Lyuzhou Chen Xi Zhang Xiurong Shen Wan Zhou Shandong Ye Chao Chen Shilu Zhang Li Zhang Yang Chen Ya Miao Wei Wang |
| author_sort | Sijia Chu |
| collection | DOAJ |
| description | Background: Patients with diabetes are more likely to be predisposed to fractures compared to those without diabetes. In clinical practice, predicting fracture risk in diabetics is still difficult because of the limited availability and accessibility of existing fracture prediction tools in the diabetic population. The purpose of this study was to develop and validate models using machine learning (ML) algorithms to achieve high predictive power for fracture in patients with diabetes in China. Methods: In this study, the clinical data of 775 hospitalized patients with diabetes was analyzed by using Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost) and Probabilistic Classification Vector Machines (PCVM) algorithms to construct risk prediction models for fractures. Moreover, the risk factors for diabetes-related fracture were identified by the feature selection algorithms. Results: The ML algorithms extracted 17 most relevant factors from raw clinical data to maximize the accuracy of the prediction results, including bone mineral density, age, sex, weight, high-density lipoprotein cholesterol, height, duration of diabetes, total cholesterol, osteocalcin, N-terminal propeptide of type I, diastolic blood pressure, and body mass index. The 7 ML models including LR, SVM, RF, DT, GBDT, XGBoost, and PCVM had f1 scores of 0.75, 0.83, 0.84, 0.85, 0.87, 0.88, and 0.97, respectively. Conclusions: This study identified 17 most relevant risk factors for diabetes-related fracture using ML algorithms. And the PCVM model proved to perform best in predicting the fracture risk in the diabetic population. This work proposes a cheap, safe, and extensible ML algorithm for the precise assessment of risk factors for diabetes-related fracture. |
| first_indexed | 2024-03-12T21:36:09Z |
| format | Article |
| id | doaj.art-0b9fb4aec97f4b5f871482678b7969fb |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-12T21:36:09Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-0b9fb4aec97f4b5f871482678b7969fb2023-07-27T05:58:50ZengElsevierHeliyon2405-84402023-07-0197e18186Machine learning algorithms for predicting the risk of fracture in patients with diabetes in ChinaSijia Chu0Aijun Jiang1Lyuzhou Chen2Xi Zhang3Xiurong Shen4Wan Zhou5Shandong Ye6Chao Chen7Shilu Zhang8Li Zhang9Yang Chen10Ya Miao11Wei Wang12Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China; Graduate School, Wannan Medical College, Wuhu, ChinaDepartment of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, ChinaSchool of Data Science, University of Science and Technology of China, Hefei, ChinaDepartment of Endocrinology, The People's Hospital of Chizhou, Chizhou, ChinaFuyang People's Hospital, Fuyang, ChinaDepartment of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, ChinaDepartment of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, ChinaDepartment of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, ChinaDepartment of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China; Graduate School, Wannan Medical College, Wuhu, ChinaDepartment of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China; Graduate School, Wannan Medical College, Wuhu, ChinaDepartment of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China; Graduate School, Anhui Medical University, Hefei, ChinaInstitution of Advanced Technology, University of Science and Technology of China, Hefei, ChinaDepartment of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China; Corresponding author. Department of Endocrinology, The First Affiliated Hospital of USTC, University of Science and Technology of China Hefei, Anhui 230001, China.Background: Patients with diabetes are more likely to be predisposed to fractures compared to those without diabetes. In clinical practice, predicting fracture risk in diabetics is still difficult because of the limited availability and accessibility of existing fracture prediction tools in the diabetic population. The purpose of this study was to develop and validate models using machine learning (ML) algorithms to achieve high predictive power for fracture in patients with diabetes in China. Methods: In this study, the clinical data of 775 hospitalized patients with diabetes was analyzed by using Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost) and Probabilistic Classification Vector Machines (PCVM) algorithms to construct risk prediction models for fractures. Moreover, the risk factors for diabetes-related fracture were identified by the feature selection algorithms. Results: The ML algorithms extracted 17 most relevant factors from raw clinical data to maximize the accuracy of the prediction results, including bone mineral density, age, sex, weight, high-density lipoprotein cholesterol, height, duration of diabetes, total cholesterol, osteocalcin, N-terminal propeptide of type I, diastolic blood pressure, and body mass index. The 7 ML models including LR, SVM, RF, DT, GBDT, XGBoost, and PCVM had f1 scores of 0.75, 0.83, 0.84, 0.85, 0.87, 0.88, and 0.97, respectively. Conclusions: This study identified 17 most relevant risk factors for diabetes-related fracture using ML algorithms. And the PCVM model proved to perform best in predicting the fracture risk in the diabetic population. This work proposes a cheap, safe, and extensible ML algorithm for the precise assessment of risk factors for diabetes-related fracture.http://www.sciencedirect.com/science/article/pii/S240584402305394XDiabetesFeature selectionMachine learningFractureRisk prediction |
| spellingShingle | Sijia Chu Aijun Jiang Lyuzhou Chen Xi Zhang Xiurong Shen Wan Zhou Shandong Ye Chao Chen Shilu Zhang Li Zhang Yang Chen Ya Miao Wei Wang Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China Heliyon Diabetes Feature selection Machine learning Fracture Risk prediction |
| title | Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China |
| title_full | Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China |
| title_fullStr | Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China |
| title_full_unstemmed | Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China |
| title_short | Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China |
| title_sort | machine learning algorithms for predicting the risk of fracture in patients with diabetes in china |
| topic | Diabetes Feature selection Machine learning Fracture Risk prediction |
| url | http://www.sciencedirect.com/science/article/pii/S240584402305394X |
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