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Integrated machine learning and deep learning for predicting diabetic nephropathy model construction, validation, and interpretability

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Abstract

Objective

To construct a risk prediction model for assisted diagnosis of Diabetic Nephropathy (DN) using machine learning algorithms, and to validate it internally and externally.

Methods

Firstly, the data was cleaned and enhanced, and was divided into training and test sets according to the 7:3 ratio. Then, the metrics related to DN were filtered by difference analysis, Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination (RFE), and Max-relevance and Min-redundancy (MRMR) algorithms. Ten machine learning models were constructed based on the key variables. The best model was filtered by Receiver Operating Characteristic (ROC), Precision-Recall (PR), Accuracy, Matthews Correlation Coefficient (MCC), and Kappa, and was internally and externally validated. Based on the best model, an online platform had been constructed.

Results

15 key variables were selected, and among the 10 machine learning models, the Random Forest model achieved the best predictive performance. In the test set, the area under the ROC curve was 0.912, and in two external validation cohorts, the area under the ROC curve was 0.828 and 0.863, indicating excellent predictive and generalization abilities.

Conclusion

The model has a good predictive value and is expected to help in the early diagnosis and screening of clinical DN.

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Data availability

All data in this article can be found in the following databases: NPHDC, NHANES, and TWBB. An online platform has been created and you can access it through the following link (https://dn-prediction.shinyapps.io/DN-PRED-English).

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Acknowledgements

We thank the National Population Health Data Center of China, the National Health and Nutrition Examination Survey of the United States, and Taiwan Biobank for providing data support.

Funding

This study was supported by the College Students’ Innovative Entrepreneurial Training Plan Program (202310367071).

Author information

Authors and Affiliations

  1. Department of Clinical Medicine, Bengbu Medical University, Bengbu, China

    Junjie Ma, Shaoguang An & Mohan Cao

  2. Department of Oncology Surgery, the Second Affiliated Hospital of Bengbu Medical University, Bengbu, China

    Lei Zhang

  3. Anhui Key Laboratory of Computational Medicine and Intelligent Health, Bengbu Medical University, Bengbu, China

    Jin Lu

  4. School of Basic Medicine, Bengbu Medical University, Bengbu, China

    Jin Lu

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  1. Junjie Ma
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Contributions

All authors contributed to the study’s conception and design. Data collection and analysis were performed by J.J.M. The first draft of the manuscript was written by J.J.M., S.G.A., and M.H.C. The revision of the manuscript was completed by L.Z. and J.L. All authors read and approved the final manuscript.

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Correspondence to Jin Lu.

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Ma, J., An, S., Cao, M. et al. Integrated machine learning and deep learning for predicting diabetic nephropathy model construction, validation, and interpretability. Endocrine (2024). https://doi.org/10.1007/s12020-024-03735-1

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