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A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method
Open accessAbstract
Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques.
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Theoretical and Empirical Analysis of ReliefF and RReliefF
Robnik-Šikonja, M., & Kononenko, I. (2003) · Machine Learning · doi:10.1023/a:1025667309714 · Cited by 3,025
Using AUC and accuracy in evaluating learning algorithms
Huang, J., & Ling, C. X. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.50 · Cited by 2,148
Cited by
Identification of significant features and data mining techniques in predicting heart disease
Amin, M. S., Chiam, Y. K., & Varathan, K. D. (2019) · Telematics and Informatics · doi:10.1016/j.tele.2018.11.007 · Cited by 528
An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier
Khan, M. A. (2020) · IEEE Access · doi:10.1109/access.2020.2974687 · Cited by 346
Mishra, J., Tiwari, M., Singh, S. T., & Goswami, S. (2022) · 2021 4th International Conference on Recent Trends in Computer Science and Technology (ICRTCST) · doi:10.1109/icrtcst54752.2022.9782009 · Cited by 4
Metadata
- Authors
- Liu, X., Wang, X., Su, Q., Zhang, M., Zhu, Y., Wang, Q., & Wang, Q.
- Venue
- Computational and Mathematical Methods in Medicine
- Publisher
- Wiley
- Published
- 2017
- Volume
- 2017
- Pages
- 1-11
- ISSN
- 1748-670X (ISSN-L)
- 1748-6718
- OpenAlex
- W2566909686
- Cited by
- 181
- References
- 2
- FWCI
- 18.14
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Liu, X., Wang, X., Su, Q., Zhang, M., Zhu, Y., Wang, Q., & Wang, Q. (2017). A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method. Computational and Mathematical Methods in Medicine. https://doi.org/10.1155/2017/8272091
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