A Research on Ensemble Deep Learning Techniques for Predicting Heart Diseases
DOI : 10.46335/IJIES.2024.9.1.3
Authors : Deepak Yashwantrao Bhadane , Dr. Indrabhan S. Borse
Abstract – Accurate diagnosis is essential for early identification and effective treatment of heart disease, which is a significant worldwide public health issue. By using ensemble deep learning techniques, this effort seeks to improve the forecast accuracy of cardiac disease. Many deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and gradient boosting machines (GBMs), may be assembled into an ensemble model. By leveraging the strengths of each model, the ensemble model predicts outcomes better than solo models. The analysts additionally look at feature engineering methods like feature extraction and selection to boost the prediction power of the models. Heart disease is a global health concern, thus it's critical to correctly predict its recurrence in order to identify it early and take the necessary measures.
