Key result
Neural ODE matches RNN models with ~87% accuracy for predicting heart disease from lipid profiles.
Why the study?
Heart disease remains a major public health concern, creating a need for reliable computational methods to assist in risk classification using clinical measurements.
Both Neural ODE and RNN models, when combined with ODE-based feature engineering and SMOTE, demonstrated high predictive performance for heart disease classification based on lipid profiles, achieving ROC-AUC values above 0.94.
Neural ODE and RNN models show comparable lipid-based prediction performance; leaves open clinical utility pending external validation.
Heart disease remains a major public health concern, creating a need for reliable computational methods that can assist in risk classification using available clinical measurements. In this study heart disease prediction combines ordinary differential equation (ODE) based feature engineering, the Synthetic Minority Oversampling Technique (SMOTE), and Recurrent Neural Network (RNN). The predictive variables include age, gender, total cholesterol (T.CHL), low-density lipoprotein cholesterol (LDL), high density lipoprotein cholesterol (HDL), and triglycerides (TRI). SMOTE were incorporated to address class imbalance. Random forest hyperparameters are optimized using GridSearchCV with stratified five-fold cross-validation and ROC-AUC as the optimization criterion.
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Muthaiyan et al. (2026) studied Heart disease (n=540). Neural ODE and RNN models vs. Each other was evaluated on ROC-AUC for heart disease prediction. The Neural ODE model achieved an accuracy of 87.0% and ROC-AUC of 0.946, while the RNN model achieved an accuracy of 86.1% and ROC-AUC of 0.947 for heart disease prediction using lipid profiles.
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