Details

Autor(en) / Beteiligte

• Gao, Xiao-Yan
• Amin Ali, Abdelmegeid
• Shaban Hassan, Hassan
• Anwar, Eman M.
• Khalil, Ahmed Mostafa
• Ahmed Mostafa Khalil

Titel

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

Ist Teil von

• Complexity (New York, N.Y.), 2021, Vol.2021, p.1-10

Ort / Verlag

Hoboken: Hindawi

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• 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.