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Development of a web platform for predicting fall risk in cardiovascular patients using machine learning
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  • Published: 02 April 2026

Development of a web platform for predicting fall risk in cardiovascular patients using machine learning

  • Jiayang Dong1 na1,
  • Xinyue Yang1 na1,
  • Zhiqiang Zhang1 na1,
  • Jiayi Sun2,
  • Wenjuan Zhang1,
  • Huihui Wang3 &
  • …
  • Cuihua Wang4 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Cardiology
  • Health care
  • Risk factors

Abstract

The aim of this study was to develop and validate a machine learning-based fall risk prediction model for middle-aged and elderly individuals with cardiovascular disease. Data were sourced from the China Health and Retirement Longitudinal Study. Key predictive variables were identified using Least Absolute Shrinkage and Selection Operator regression. Six machine learning algorithms were employed to construct predictive models. Model performance was assessed through both internal and external validation, with SHapley Additive exPlanation values used to interpret the optimal model. A total of 1784 participants were analyzed, of which 434 (24.3%) experienced falls during a two-year follow-up period. Nine predictive factors were incorporated into the model, with the Light Gradient Boosting Machine model demonstrating the best performance, achieving areas under the receiver operating characteristic curve of 0.839 (95% CI 0.804–0.874) for internal validation and 0.816 (95% CI 0.798–0.833) for external validation. This model provides a scientific basis for personalized fall prevention strategies aimed at reducing fall incidence and enhancing the quality of life for patients with cardiovascular disease. Finally, we formed a web platform based on the best model to predict the probability of falls in cardiovascular patients.

Data availability

All data are available from the CHARLS portal site (https://charls.pku.edu.cn/).

Abbreviations

CVD:

Cardiovascular disease

CHARLS:

China Health and Retirement Longitudinal Survey

WC:

Waist circumference

BMI:

Body mass index

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

CES-D:

Center for epidemiologic studies depression scale

ADL:

Activities of daily living

SMOTE:

Synthetic minority over-sampling technique

LASSO:

Least absolute shrinkage and selection operator

SHAP:

SHapley additive exPlanations

ROC:

Receiver operating characteristic

AUC:

Area under the receiver operating characteristic curve

PPV:

Positive predictive value

NPV:

Negative predictive value

RF:

Random forest

LightGBM:

Light gradient boosting machine

LR:

Logistic regression

XGBoost:

Extreme gradient boosting

SVM:

Support vector machine

DNN:

Deep neural network

CKD:

Chronic kidney disease

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Acknowledgements

This article is primarily supported by the China Health and Retirement Longitudinal Study. We would also like to thank the participants for their valuable contributions and the research teams involved in data collection and management.

Funding

This research was supported by the S&T Program of Hebei (20377787D); Hebei Provincial Department of Science and Technology—Central Government Guided Local Science and Technology Development Fund Project (246Z7738G); Hebei Provincial Government-Funded Clinical Medicine Outstanding Talent Training Project (ZF2025068); Hebei Provincial Health Commission Research Project (20240495).

Author information

Author notes

  1. Jiayang Dong, Xinyue Yang and Zhiqiang Zhang have contributed equally to this work.

Authors and Affiliations

  1. Department of Cardiology, Tianjin Medical University General Hospital, Tianjin, China

    Jiayang Dong, Xinyue Yang, Zhiqiang Zhang & Wenjuan Zhang

  2. Department of Cardiology, Tianjin Chest Hospital, Tianjin, China

    Jiayi Sun

  3. Neurology Rehabilitation Group, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China

    Huihui Wang

  4. Cardiac Rehabilitation Group, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China

    Cuihua Wang

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Contributions

C.H.W. and H.H.W. primarily contributed to revisions of the manuscript and funding acquisition. J.Y.D., X.Y.Y. and Z.Q.Z. was mainly involved in manuscript writing, and data processing. J.Y.S. primarily contributed to literature retrieval and data processing. All authors agreed on the final version of the manuscript.

Corresponding authors

Correspondence to Huihui Wang or Cuihua Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The CHARLS was approved by the Institutional Review Board of Peking University (IRB00001052-11015) and all participants gave written informed consent.

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Cite this article

Dong, J., Yang, X., Zhang, Z. et al. Development of a web platform for predicting fall risk in cardiovascular patients using machine learning. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43482-z

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  • Received: 20 February 2025

  • Accepted: 04 March 2026

  • Published: 02 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-43482-z

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Keywords

  • Fall
  • Cardiovascular disease
  • Machine learning
  • Prediction model
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