Heart Failure Detection Using Scaled Conjugate Gradient Method and Naïve Bayes

Abstract

Heart failure known as high mortality rates is a serious pathophysiological condition characterized and substantial long-term healthcare costs. Early detection is crucial, as the disease tends to progress without timely and appropriate intervention. This study aims to predict the risk of heart failure using structured clinical data and to leverage deep learning techniques to enhance the accuracy of risk assessment. The core objective is to demonstrate that early identification of heart failure indicators can significantly improve patient outcomes, potentially distinguishing between life and death. Recognizing these early warning signs provides a better opportunity for preventive care and timely treatment. To achieve this, two algorithms were employed: the Scaled Conjugate Gradient method within an Artificial Neural Network (ANN) framework, and the Naïve Bayes classifier. A Feed-Forward Neural Network (FFNN) was utilized as the primary classifier to detect the presence of heart failure. The neural network architecture used in this study consisted of 12 input neurons, 20 hidden layers, and a single output layer. The performance results revealed that the ANN achieved an accuracy of 86.7%, while the Naïve Bayes classifier reached an accuracy of 76.9%. Overall, the ANN demonstrated best performance in detecting heart failure, especially with a large number of hidden neurons, highlighting its potential as an effective diagnostic tool.