Advanced Prediction of Heart Failure Risk in Elderly Diabetic and Hypertensive Patients Using Nine Machine Learning Models and Novel Composite Indices: Insights from NHANES 2003-2016

Abstract

Aim: As the global population ages, cardiovascular diseases, particularly heart failure (HF), have become leading causes of mortality and disability among elderly patients. Diabetes and hypertension are major risk factors for cardiovascular diseases, making this group especially vulnerable to heart failure. Current clinical tools for predicting HF risk are often complex, requiring extensive clinical parameters and laboratory tests, which limit their practical application. Therefore, a need exists for a predictive model that is both simple and effective in assessing heart failure risk in elderly patients with diabetes and hypertension.

Methods and results: This study utilized data from the National Health and Nutrition Examination Survey (NHANES), spanning seven cycles from 2003 to 2016, including 71,058 subjects. The study focused on elderly patients (aged 65 and above) diagnosed with both diabetes and hypertension, ultimately including 1,445 participants. We examined seven novel composite indices: A Body Shape Index (ABSI), Atherogenic Index of Plasma (AIP), BARD score, Body Fat Percentage (BFP), Body Roundness Index (BRI), Fatty Liver Index (FLI), and Prognostic Nutritional Index (PNI). These indices were selected for their simplicity and ease of calculation from routine clinical assessments. The primary outcome was heart failure status, and data preprocessing included imputation for missing values using random forest algorithms. Various machine learning models were applied, including Random Forest, Logistic Regression, XGBoost, and others, with model performance assessed through metrics like accuracy, precision, recall, F1 score, and ROC AUC. The best-performing model was further analyzed using SHAP (SHapley Additive exPlanations) values to determine feature importance. The study found that the XGBoost model demonstrated superior performance across all evaluation metrics, with an AUC value of 0.96. Significant predictors of heart failure included BRI and PNI, which had the highest SHAP values, indicating their substantial influence on model predictions. The study also highlighted the robust predictive capabilities of AIP, particularly in assessing cardiovascular events in elderly patients.

Conclusion: The study demonstrates that novel composite indices like ABSI, AIP, BARD score, Body Fat Percentage, BRI, FLI, and PNI have significant potential in predicting heart failure risk among elderly diabetic and hypertensive patients. These indices offer clinicians new tools for cardiovascular risk assessment that are simpler and potentially more effective in clinical practice. Future research should focus on validating these findings in different populations and exploring their longitudinal predictive power.

Keywords: Diabetes; Heart failure; Hypertension; Machine learning; NHANES; Novel composite indices.