Abstract
Heart disease (HD) is one of the most common diseases throughout the world. In this article, we describe an innovative AI system and ensemble strategy that can accurately identify HD and apply it to the information collected from the UCI AI repository. We tried six traditional machine learning algorithms (SVM, DT, KNN, RF, Adaboost and LR), and two voting classifiers (SVC, KNN, DT, RF, ADA and SVC, KNN, RF) as ensemble algorithm. In order to improve the representation and comparison of these algorithms, the data was standardized. The introduced method improves the representation of all conventional machine learning algorithms used in this survey. We propose voting classifiers 1 and 2 as ensemble algorithms for machine learning algorithms. Our research shows that RF creates an accuracy of 88.52% in a set of machine learning classifiers, while Voting Classifier 2 provides an accuracy of 86.88%, while predicting that it will be recorded as a well-known UCI AI dataset. We concluded that machine learning programs upgraded through the suggested methods can suggest unusually accurate models that are planned for clinical use and examination.
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Aggarwal, R., Pal, S. (2021). Comparison of Machine Learning Algorithms and Ensemble Technique for Heart Disease Prediction. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_126
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