LASSO-based machine learning algorithm to predict the incidence of diabetes in different stages

Abstract

Background

Formal risk assessment is crucial for diabetes prevention. We aimed to establish a practical nomogram for predicting the risk incidence of prediabetes and prediabetes conversion to diabetes.

Methods

A cohort of 1428 subjects was collected to develop prediction models. The LASSO was used to screen for important risk factors in prediabetes and diabetes and was compared with other algorithms (LR, RF, SVM, LDA, NB, and Treebag). Multivariate logistic regression analysis was used to construct the prediction model of prediabetes and diabetes, and drawn the predictive nomogram. The performance of the nomograms was evaluated by receiver-operating characteristic curve and calibration.

Results

These findings revealed that the other six algorithms were not as good as LASSO in terms of diabetes risk prediction. The nomogram for individualized prediction of prediabetes included “Age,” “FH,” “Insulin_F,” “hypertension,” “Tgab,” “HDL-C,” “Proinsulin_F,” and “TG” and the nomogram of prediabetes to diabetes included “Age,” “FH,” “Proinsulin_E,” and “HDL-C”. The results showed that the two models had certain discrimination, with the AUC of 0.78 and 0.70, respectively. The calibration curve of the two models also indicated good consistency.

Conclusions

We established early warning models for prediabetes and diabetes, which can help identify prediabetes and diabetes high-risk populations in advance.

Keywords:

• Diabetes
• prediabetes
• LASSO
• machine learning
• nomogram

Acknowledgments

The authors are grateful to the case and control volunteers in the study. The authors also thank to the clinicians and hospital staff who contributed to the sample and data collection for our study.

Ethical approval and consent to participate

All participants were informed in writing and verbally of the procedures and purpose of the study and signed informed consent document. Study protocols were approved by the Ethical Committee of the First Affiliated Hospital of Hainan Medical University, and complied with the ethical standards of the Ethical Committee and World Medical Association Declaration of Helsinki. All research analyses were carried out in accordance with the approved guidelines and regulations.

Author contribution

Huibiao Quan: Conceptualization, Methodology, Software. Leweihua Lin and Danhong Lin: Data curation. Danhong Lin: Visualization, Investigation. Kaining Chen: Software, Validation.Qianying Ou and Wei Jin: Writing-Original draft preparation, Writing-Reviewing and Editing.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by Hainan Provincial Key Research and Development Project (No. ZDYF2022SHFZ316 and ZDYF2021SHFZ078).