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Identification of influence factors in overweight population through an interpretable risk model based on machine learning: a large retrospective cohort

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Abstract

Background

The identification of associated overweight risk factors is crucial to future health risk predictions and behavioral interventions. Several consensus problems remain in machine learning, such as cross-validation, and the resulting model may suffer from overfitting or poor interpretability.

Methods

This study employed nine commonly used machine learning methods to construct overweight risk models. The general community are the target of this study, and a total of 10,905 Chinese subjects from Ningde City in Fujian province, southeast China, participated. The best model was selected through appropriate verification and validation and was suitably explained.

Results

The overweight risk models employing machine learning exhibited good performance. It was concluded that CatBoost, which is used in the construction of clinical risk models, may surpass previous machine learning methods. The visual display of the Shapley additive explanation value for the machine model variables accurately represented the influence of each variable in the model.

Conclusions

The construction of an overweight risk model using machine learning may currently be the best approach. Moreover, CatBoost may be the best machine learning method. Furthermore, combining Shapley’s additive explanation and machine learning methods can be effective in identifying disease risk factors for prevention and control.

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Data availability

The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.

Abbreviations

ANN/MLP:

artificial neural network/multiparametric linear programming

AUC:

area under the curve

BMI:

body mass index

BP:

blood pressure

DM:

diabetes mellitus

DPB:

diastolic blood pressure

FBG:

fasting blood glucose

FINS:

fasting insulin

GBDT:

gradient boosted decision tree

GBM:

gradient boosting machine

GNB:

Gaussian NB

HDL-C:

high-density lipoprotein cholesterol

HOMA-IR:

homeostasis model assessment of insulin resistance

KNN:

K-nearest neighbor

LDL-C:

low-density lipoprotein cholesterol

PBG:

postprandial blood glucose

ROC:

receiver operating characteristic

SBP:

systolic blood pressure

SHAP:

Shapley additive explanation

SVM:

supported vector machine

TC:

total cholesterol

TG:

total triglyceride

WC:

waist circumference

WHO:

World Health Organization

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Acknowledgements

The authors would like to thank the participants for providing the information used in this study and for kindly making arrangements for the data collection.

Funding

This study was supported by Fujian Research and Training Grants for Young and Middle-aged Leaders in Healthcare (Grant No. (2023)417#), the Innovation Project of Fujian Provincial Health Commission (2021CXA003), Natural Science Foundation of Fujian Province (Grant No. 2022J011017 and Grant No. 2020J011068), National Key Research and Development Program of China (2018YFC2001100-5), and Natural Science Foundation of China (82070878).

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Author notes

  1. These authors contributed equally: Wei Lin, Songchang Shi

Authors and Affiliations

  1. Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, FuZhou, 350001, PR China

    Wei Lin, Huiyu Lan, Nengying Wang, Huibin Huang, Junping Wen & Gang Chen

  2. Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Hospital Jinshan Branch, Fujian Provincial Hospital, Fuzhou, 350001, PR China

    Songchang Shi

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Contributions

W.L. and S.S. performed the formal analysis, devised the methodology, and wrote the original draft. H.L., H.H., and J.W. performed the curation of data and resources. W.L. and G.C. were involved in the conceptualization, formal analysis, writing of the original draft, and project administration. G.C. are the guarantors of this manuscript, had full access to all the data in the study, and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Corresponding authors

Correspondence to Wei Lin or Gang Chen.

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The authors declare no competing interests.

Ethical approval

Our study was performed in accordance with the Declaration of Helsinki and approved by The Ethics Committee of Fujian Provincial Hospital (approval no. K2019-06-032). All patients provided written informed consent prior to enrollment in the study.

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Lin, W., Shi, S., Lan, H. et al. Identification of influence factors in overweight population through an interpretable risk model based on machine learning: a large retrospective cohort. Endocrine 83, 604–614 (2024). https://doi.org/10.1007/s12020-023-03536-y

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