Analysis of the Impact of Backpropagation Hyperparameter Optimization on Heart Disease Prediction Models

Heart disease is a major global health issue, highlighting the need for early and accurate prediction to reduce complications and improve patient outcomes. The Backpropagation Neural Network (BPNN) is a widely used method for heart disease prediction, but its performance relies heavily on proper hyperparameter selection, including neuron count, activation function, optimizer, and batch size. This study analyzed the impact of hyperparameter optimization on BPNN performance. A standard BPNN model was compared with an optimized version, where key hyperparameters were fine-tuned to enhance predictive accuracy and stability. Both models were trained and tested on the same dataset, and their performance was evaluated using Accuracy, Precision, Recall, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The results show that the optimized model achieves a slightly better accuracy (99.11% vs. 99.09%) and lower error rates (MSE and MAE of 0.0089 vs. 0.0091). It also demonstrates higher precision, reflecting an improved capability in correctly identifying heart disease cases. Although the performance gap was small, the optimized model showed a more balanced and consistent outcome. These findings highlight the importance of hyperparameter tuning for improving neural network models for medical prediction. This study contributes to the development of more accurate and reliable AI tools for the early diagnosis of heart disease. Future studies may apply advanced optimization techniques, such as Bayesian Optimization or Genetic Algorithms, and use larger and more diverse datasets to enhance model generalization.