Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble

Abstract

Coronary heart disease (CHD) is one of the severe health issues and is one of the most common types of heart diseases. It is the most frequent cause of mortality across the globe due to the lack of a healthy lifestyle. Owing to the fact that a heart attack occurs without any apparent symptoms, an intelligent detection method is inescapable. In this article, a new CHD detection method based on a machine learning technique, e.g., classifier ensembles, is dealt with. A two-tier ensemble is built, where some ensemble classifiers are exploited as base classifiers of another ensemble. A stacked architecture is designed to blend the class label prediction of three ensemble learners, i.e., random forest, gradient boosting machine, and extreme gradient boosting. The detection model is evaluated on multiple heart disease datasets, i.e., Z-Alizadeh Sani, Statlog, Cleveland, and Hungarian, corroborating the generalisability of the proposed model. A particle swarm optimization-based feature selection is carried out to choose the most significant feature set for each dataset. Finally, a two-fold statistical test is adopted to justify the hypothesis, demonstrating that the performance differences of classifiers do not rely upon an assumption. Our proposed method outperforms any base classifiers in the ensemble with respect to 10-fold cross validation. Our detection model has performed better than current existing models based on traditional classifier ensembles and individual classifiers in terms of accuracy, F1, and AUC. This study demonstrates that our proposed model adds a considerable contribution compared to the prior published studies in the current literature.

Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis

“…Additionally, to further validate the performance of the proposed method, we compare it with some models for heart disease prediction available in the recent literature, including a feature selection method using PSO and Softmax regression [13], a two-tier ensemble method with PSO-based feature selection [14], an ensemble classifier comprising of the following base learners: NB, Bayes Net (BN), RF, and MLP [27], a hybrid method of NB and LR [28], and a hybrid RF with a linear model (HRFLM) [29]. The other techniques include a combination of LR and Lasso regression [30], an intelligent heart disease detection method based on NB and advanced encryption standard (AES) [31], a combination of ANN and Fuzzy analytic hierarchy method (Fuzzy-AHP) [32], and a sparse autoencoder feature learning method combined ANN classifier [12].…”

“…The sparse autoencoder (SAE) is an unsupervised learning method which is used to automatically learn features from unlabeled data [14]. In this type of autoencoder, the training criterion involves a sparsity penalty.…”

“…Verma et al [13] PSO and Softmax regression 88.4 Tama et al [14] Ensemble and PSO 85.71 Latha and Jeeva [27] An Ensemble of NB, BN, RF, and MLP 85.48 Amin et al [28] A hybrid NB and LR 87.4 Mohan et al [29] HRFLM 88.4 Haq et al [30] LASSO-LR Model 89 Repaka et al [31] NB-AES 89.77 Samuel et al [32] ANN-Fuzzy-AHP 91 Mienye et al [12] SAE+ANN 90 Our approach Improved SAE + Softmax 91…”

“…Tama et al [14] implemented an ensemble learning method for the diagnosis of heart disease. The ensemble method was developed via a stacked structure, whereby the base learners were also ensembles.…”

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

“…Current researchers are finding it difficult to combine these features with the appropriate machine learning techniques to make an accurate prediction of heart disease [21]. Machine learning algorithms are most effective when they are trained on suitable datasets [22][23][24][25]. Since the algorithms rely on the consistency of the training and test data, the use of feature selection techniques such as data mining, Relief selection, and LASSO can help to prepare the data in order to provide a more accurate prediction.…”