LLM Machine Learning for Predicting Cardiovascular Mortality in Patients
DOI:
https://doi.org/10.5281/zenodo.14015999Keywords:
cardiovascular mortality, chronic kidney disease, machine learning, interpretable model, shapAbstract
Patients with chronic kidney disease (CKD) face a high risk of cardiovascular death, yet accurately predicting this risk remains challenging. This study aims to develop an interpretable machine learning (ML) model to predict 10-year cardiovascular mortality in CKD patients using SHAP explainers. [1]Six ML models were created and tested, with the best model selected for prediction and patient categorization. Survival rates were analyzed using log-rank tests on Kaplan-Meier curves, and Cox regression was employed to explore the relationship between ML-predicted risk scores and mortality. The chosen autoencoder (AE) model demonstrated superior performance, with higher ML scores[2] significantly correlating with increased cardiovascular mortality risk. Key determinants such as age, high blood pressure, C-reactive protein, and serum creatinine were identified. The ML-driven tool showcased high accuracy in determining the 10-year cardiovascular mortality risk for CKD patients, offering valuable insights for individual risk assessments.
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Copyright (c) 2024 Yue Zhu, Xiaoyi Zhang, Yuechen Zhang
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