Dual-Layer Deep Ensemble Techniques for Classifying Heart Disease





Deep Learning, Ensemble Techniques, Heart Disease, Machine Learning, Multiple Classifiers, Stacking Ensemble


The prevalence of heart disease is increasing at a rapid rate due to changes in food habits and lifestyle of people
all over the world. Early prediction and diagnosis of this fatal disease is a highly excruciating task. Nowadays, the
ensemble learning approaches are preferred owing to their effectiveness in performance when compared to the
performance of a single classification algorithm. In this work, a Dual-Layer Stacking Ensemble (DLSE) technique
and a Deep Heterogeneous Ensemble (DHE) technique to classify heart disease are proposed. The DLSE uses several heterogeneous classifiers to form an ensemble that is efficient as well as diverse. The proposed framework
consists of two layers with the first layer consisting of three different base learning algorithms Naïve Bayes (NB),
Decision Tree (DT), and Support Vector Machine (SVM). The second layer comprises of three different classifiers, Extremely Randomized Trees (ERT), Ada Boost Classifier (ABC) and Random Forest (RF). The second layer
utilizes the results from the first layer to provide a diverse input for the three classifiers. Finally, the outcomes
are fed to the meta-classifier Gradient Boosted Trees (GBT) to generate the final prediction. The DHE uses three
deep learning models Convolutional Neural Networks with Bidirectional Long Short-Term Memory (CNN BiLSTM), Artificial Neural Network (ANN) and Recurrent Neural Network (RNN) with RF, ERT and GBT as the
meta-learners. The performance of the proposed methods is compared with traditional state-of-the-art classifiers
as well as existing ensemble learning and deep learning methods. The experimental outcomes show that the proposed DLSE and DHE methods perform exceptionally in terms of accuracy, precision and recall measures.