Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method

• Published in: Complexity (New York, N.Y.) Vol. 2021; no. 1
• Main Authors: Gao, Xiao-Yan, Amin Ali, Abdelmegeid, Shaban Hassan, Hassan, Anwar, Eman M.
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi 2021; John Wiley & Sons, Inc; Wiley
• Subjects: Accuracy; Age; Algorithms; Angina pectoris; Back propagation; Blood pressure; Cardiovascular disease; Data collection; Data mining; Datasets; Decision trees; Discriminant analysis; Feature extraction; Feature selection; Heart diseases; Heart rate; Literature reviews; Machine learning; Model accuracy; Neural networks; Pain; Performance prediction; Principal components analysis; Support vector machines
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2021/6663455

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.