Identification of cardiovascular disease in patients with kidney stone disease using explainable machine learning - Takeaways - MDSpire

Identification of cardiovascular disease in patients with kidney stone disease using explainable machine learning

  • By

  • Qinglong Yang

  • Nan Luo

  • Hanyuan Lin

  • Haolin Chen

  • Haoxian Tang

  • Jingtao Huang

  • Xuan Zhang

  • Wenqiang Liao

  • Yuxue Lin

  • Zexuan Liu

  • Xuxia Sui

  • Qingtao Yang

  • Gaoming Hou

  • May 29, 2026

  • 0 min

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  • 1

    Kidney stone disease is linked to a 47% increased risk of cardiovascular disease (CVD) compared to those without kidney stones.

  • 2

    The study utilized NHANES data from 34,770 participants to validate the association between kidney stones and CVD.

  • 3

    A logistic regression model demonstrated strong performance in identifying prevalent CVD in patients with kidney stone disease.

  • 4

    The Shapley Additive exPlanation (SHAP) method identified 15 important predictors for CVD in individuals with kidney stones.

  • 5

    This research emphasizes the need for targeted CVD risk assessment tools for patients with kidney stone disease.

Original Source(s)

Identification of cardiovascular disease in patients with kidney stone disease using explainable machine learning

  • by Qinglong Yang, Nan Luo, Hanyuan Lin, Haolin Chen, Haoxian Tang, Jingtao Huang, Xuan Zhang, Wenqiang Liao, Yuxue Lin, Zexuan Liu, Xuxia Sui, Qingtao Yang, Gaoming Hou

  • May 29, 2026

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