The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning
Open accessAbstract
The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.
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Citations
Cites
Understanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S., & Ben-David, S. (2014) · doi:10.1017/cbo9781107298019 · Cited by 3,115
Effective diagnosis of heart disease through neural networks ensembles
Das, R., Turkoglu, I., & Sengur, A. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.013 · Cited by 628
Cited by
Machine-learning-based disease diagnosis: a comprehensive review
Ahsan, M. M., Luna, S. A., & Siddique, Z. (2022) · Healthcare · doi:10.3390/healthcare10030541 · Cited by 603
Evaluation of artificial intelligence techniques in disease diagnosis and prediction
Ghaffar Nia, N., Kaplanoglu, E., & Nasab, A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00049-5 · Cited by 426
Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization
Chandrasekhar, N., & Peddakrishna, S. (2023) · Processes · doi:10.3390/pr11041210 · Cited by 268
Hybrid optimization enabled deep learning-based ensemble classification for heart disease detection
Jayasudha, R., Suragali, C., Thirukrishna, J. T., & Santhosh Kumar, B. (2023) · Signal, Image and Video Processing · doi:10.1007/s11760-023-02656-2 · Cited by 21
Metadata
- Authors
- Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., & Singh, P.
- Venue
- Computational Intelligence and Neuroscience
- Publisher
- Wiley
- Published
- 2021-01
- Volume
- 2021
- Issue
- 1
- Pages
- 8387680-8387680
- ISSN
- 1687-5265 (ISSN-L)
- 1687-5273
- OpenAlex
- W3177781640
- Cited by
- 602
- References
- 2
- FWCI
- 91.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
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