Abstract
Heart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this paper, ensemble learning methods are used to enhance the performance of predicting heart disease. Two features of extraction methods: linear discriminant analysis (LDA) and principal component analysis (PCA), are used to select essential features from the dataset. The comparison between machine learning algorithms and ensemble learning methods is applied to selected features. The different methods are used to evaluate models: accuracy, recall, precision, F-measure, and ROC.The results show the bagging ensemble learning method with decision tree has achieved the best performance.
Highlights
The cardiac disease is one of the most critical problems relating to human safety. e treatment of heart problems has recently been stated in a study that has received huge attention in the medical system worldwide
Bhatet al. [19] proposed a model that is a combination of multilayer perceptron network (MLP) with a backpropagation algorithm to diagnose heart disease. e result shows that the proposed model has reduced error and an improved accuracy of 80.99%
Sapra et al [24] utilized two datasets (Z-Alizadesh Sani and Cleveland heart disease dataset) that were trained by six machine learning algorithms (LR, deep learning (DL), decision tree (DT), random forest (RF), support vector machine (SVM), and ensemble learning) to classify cardiac diseases. e results showed that gradient boosted tree achieved the best accuracy of 84% compared to other algorithms
Summary
The cardiac disease is one of the most critical problems relating to human safety. e treatment of heart problems has recently been stated in a study that has received huge attention in the medical system worldwide. E result shows that ensemble learning has improved the prediction performance of cardiac disease compared to other algorithms. Sapra et al [24] utilized two datasets (Z-Alizadesh Sani and Cleveland heart disease dataset) that were trained by six machine learning algorithms (LR, deep learning (DL), DT, RF, SVM, and ensemble learning (gradient boosted tree)) to classify cardiac diseases. Haq et al [25] used seven machine learning algorithms: LR, ANN, KNN, NB, SVM, DT, and RF with three feature selections: minimal-redundancy-maximal-relevance (mRMR), Relief, and Shrinkage and Selection Operator (LASSO) to predict heart disease. It is worth noting that missing values are deleted from the dataset
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