Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy
The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SA...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Journal of Diabetes Research |
| Online Access: | http://dx.doi.org/10.1155/2024/8857453 |
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| _version_ | 1832559577087868928 |
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| author | Jing-Mei Yin Yang Li Jun-Tang Xue Guo-Wei Zong Zhong-Ze Fang Lang Zou |
| author_facet | Jing-Mei Yin Yang Li Jun-Tang Xue Guo-Wei Zong Zhong-Ze Fang Lang Zou |
| author_sort | Jing-Mei Yin |
| collection | DOAJ |
| description | The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN. |
| format | Article |
| id | doaj-art-b2eb26e8b97b4722a245dfa5c4fa437c |
| institution | Kabale University |
| issn | 2314-6753 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Research |
| spelling | doaj-art-b2eb26e8b97b4722a245dfa5c4fa437c2025-02-03T01:29:50ZengWileyJournal of Diabetes Research2314-67532024-01-01202410.1155/2024/8857453Explainable Machine Learning-Based Prediction Model for Diabetic NephropathyJing-Mei Yin0Yang Li1Jun-Tang Xue2Guo-Wei Zong3Zhong-Ze Fang4Lang Zou5School of Mathematics and Computational Science Xiangtan UniversityDepartment of Toxicology and Sanitary ChemistryDepartment of Toxicology and Sanitary ChemistryDepartment of MathematicsDepartment of Toxicology and Sanitary ChemistrySchool of Mathematics and Computational Science Xiangtan UniversityThe aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN.http://dx.doi.org/10.1155/2024/8857453 |
| spellingShingle | Jing-Mei Yin Yang Li Jun-Tang Xue Guo-Wei Zong Zhong-Ze Fang Lang Zou Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy Journal of Diabetes Research |
| title | Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy |
| title_full | Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy |
| title_fullStr | Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy |
| title_full_unstemmed | Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy |
| title_short | Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy |
| title_sort | explainable machine learning based prediction model for diabetic nephropathy |
| url | http://dx.doi.org/10.1155/2024/8857453 |
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