Different Deep Learning Techniques in Heart Disease Classification: Survey
DOI:
https://doi.org/10.29304/jqcm.2023.15.2.1233Keywords:
CNN/ LSTM, Heart disease, Classification, Echocardiogram, Deep learningAbstract
Cardiovascular disease prediction is a serious challenge for clinical data analysis. This study examines deep learning-based categorization strategies for heart disease. Deep learning algorithms are employed with echocardiograms to categorize heart disease. This paper uses echocardiography to predict and identify heart abnormalities, with the help of decision-making and forecasting, based on the copious data the healthcare sector has provided. Medical experts can forecast clinical outcomes, which helps them choose the best course of action. In-depth longitudinal electronic health records are a rich source of historical data with complex patterns that have the potential to be leveraged by machine learning to improve physicians' prediction abilities (EHR) significantly. Most contemporary medical specialties rely on imaging as one of the most data-rich components of electronic health records when making treatment decisions (EHRs). Only a few tasks in medical image processing and reconstruction have seen success using machine and deep learning, including registration, segmentation, and feature extraction. Cardiac imaging sequence analysis must incorporate the extraction of spatial and temporal characteristics to forecast crucial information throughout time correctly.
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References
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[3] M. Ali and A. Obied, “Interactive Situated Autonomic Multi-Agents System- Comprehensive Survey,” vol. 14, no. 3, pp. 22–32, 2022.
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[12] M. Manur, A. K. Pani, and P. Kumar, “A prediction technique for heart disease based on long short-term memory recurrent neural network,” Int. J. Intell. Eng. Syst., vol. 13, no. 2, pp. 31–39, 2020, doi: 10.22266/ijies2020.0430.04.
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[15] A. Degerli et al., “Early Detection of Myocardial Infarction in Low-Quality Echocardiography,” IEEE Access, vol. 9, pp. 34442–34453, 2021, doi: 10.1109/ACCESS.2021.3059595.
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[19] R. Muhtaseb and M. Yaqub, “EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from Spatiotemporal Echocardiography,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 13434 LNCS, pp. 370–379, 2022, doi: 10.1007/978-3-031-16440-8_36.
[20] K. Deng et al., “Trans Bridge: A Lightweight Transformer for Left Ventricle Segmentation in Echocardiography,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12967 LNCS, pp. 63–72, 2021, doi: 10.1007/978-3-030-87583-1_7.
[21] A. Dutta, T. Batabyal, M. Basu, and S. T. Acton, “An efficient convolutional neural network for coronary heart disease prediction,” Expert Syst. Appl., vol. 159, 2020, doi: 10.1016/j.eswa.2020.113408.
[22] J. Soni, U. Ansari, and D. Sharma, “Intelligent and Effective Heart Disease Prediction System using Weighted Associative Classifiers,” vol. 3, no. 6, pp. 2385–2392, 2011.
[23] A. Yazdani, K. D. Varathan, Y. K. Chiam, A. W. Malik, and W. A. Wan Ahmad, “A novel approach for heart disease prediction using strength scores with significant predictors,” BMC Med. Inform. Decis. Mak., vol. 21, no. 1, pp. 1–16, 2021, doi: 10.1186/s12911-021-01527-5.
[24] H. Yang, C. Shan, A. Bouwman, A. F. Kolen, and P. H. N. de With, “Efficient and Robust Instrument Segmentation in 3D Ultrasound Using Patch-of-Interest-FuseNet with Hybrid Loss,” Med. Image Anal., vol. 67, p. 101842, 2021, doi: 10.1016/j.media.2020.101842.
[25] M. Danu, C. F. Ciusdel, and L. M. Itu, “Deep learning models based on automatic labeling with application in echocardiography,” 2020 24th Int. Conf. Syst. Theory, Control Comput. ICSTCC 2020 - Proc., pp. 373–378, 2020, doi: 10.1109/ICSTCC50638.2020.9259701.
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[27] M. M. Kazemi Esfeh, C. Luong, D. Behnami, T. Tsang, and P. Abolmaesumi, A Deep Bayesian Video Analysis Framework: Towards a More Robust Estimation of Ejection Fraction, vol. 12262 LNCS. Springer International Publishing, 2020. doi 10.1007/978-3-030-59713-9_56.
[28] J. W. Hughes et al., “Deep learning evaluation of biomarkers from echocardiogram videos,” EBioMedicine, vol. 73, p. 103613, 2021, doi: 10.1016/j.ebiom.2021.103613.
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[31] Hughes, J. W., Yuan, N., He, B., Ouyang, J., Ebinger, J., Botting, P., ... & Zou, J. Y. (2021)." Deep learning prediction of biomarkers from echocardiogram videos," medRxiv, 2021-02.
[32] H. Reynaud, A. Vlontzos, B. Hou, A. Beqiri, P. Leeson, and B. Kainz, “Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12906 LNCS, pp. 495–505, 2021, doi: 10.1007/978-3-030-87231-1_48.
[33] D. S and A. Ravikumar, “Computation Methods for the Diagnosis and Prognosis of Heart Disease,” Int. J. Comput. Appl., vol. 95, no. 19, pp. 5–9, 2014, doi: 10.5120/16700-6832.
[34] A. Baccouche, B. Garcia-Zapirain, C. C. Olea, and A. Elmaghraby, “Ensemble deep learning models for heart disease classification: A case study from Mexico,” Inf., vol. 11, no. 4, pp. 1–28, 2020, doi: 10.3390/INFO11040207.
[35] V. Chandra, P. G. Sarkar, and V. Singh, “Mitral Valve Leaflet Tracking in Echocardiography using Custom Yolo3,” Procedia Comput. Sci., vol. 171, no. 2019, pp. 820–828, 2020, doi: 10.1016/j.procs.2020.04.089.
[36] I. Hwang, L. Ju, H. Lee, J. Park, and S. Lee, “Differential diagnosis of common etiologies of left ventricular hypertrophy using a hybrid CNN-LSTM model”. (2022). Scientific Reports, 12(1), 20998.
[37] F. T. Dezaki et al., “Deep residual recurrent neural networks for characterisation of cardiac cycle phase from echocardiograms,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10553 LNCS, pp. 100–108, 2017, doi: 10.1007/978-3-319-67558-9_12.
[38] “SEQUENTIAL ANATOMY LOCALIZATION IN FETAL ECHOCARDIOGRAPHY VIDEO Arijit Patra, J A Noble Institute of Biomedical Engineering, University of Oxford”.
[39] A. I. Shahin and S. Almotairi, “An Accurate and Fast Cardio-Views Classification System Based on Fused Deep Features and LSTM,” IEEE Access, vol. 8, pp. 135184–135194, 2020, doi: 10.1109/ACCESS.2020.3010326.
[40] F. T. Dezaki et al., “Cardiac Phase Detection in Echocardiograms with Densely Gated Recurrent Neural Networks and Global Extrema Loss,” IEEE Trans. Med. Imaging, vol. 38, no. 8, pp. 1821–1832, 2019, doi 10.1109/TMI.2018.2888807.
[41] Z. Feng, J. A. Sivak, and A. K. Krishnamurthy, “TWO-STREAM ATTENTION SPATIO-TEMPORAL NETWORK FOR CLASSIFICATION OF Department of Computer Science, 2 Division of Cardiology, 3 Renaissance Computing Institute, ; ĂͿ ; ĐͿ ; ďͿ ; ĚͿ,” pp. 1461–1465, 2021.
[42] K. Wang, X. Qi, and H. Liu, “Photovoltaic power forecasting-based LSTM-Convolutional Network,” Energy, vol. 189, p. 116225, 2019, doi 10.1016/j.energy.2019.116225.
[43] M. Blaivas and L. Blaivas, “Machine learning algorithm using publicly available echo database for simplified ‘visual estimation’ of left ventricular ejection fraction,” World J. Exp. Med., vol. 12, no. 2, pp. 16–25, 2022, doi: 10.5493/wjem. v12.i2.16.
[44] P. B. Patil, P. M. M. Shastry, and P. S. Ashokumar, “Heart Attack Detection Based on Mask Region-Based Convolutional Neural Network Instance Segmentation and Hybrid Classification Using Machine Learning Techniques,” Turkish J. Comput. Math. Educ., vol. 12, no. 9, pp. 2228–2244, 2021, [Online]. Available: https://www.proquest.com/scholarly-journals/heart-attack-detection-based-on-mask-region/docview/2623456523/se-2
[45] S. Mohan, C. Thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, vol. 7, pp. 81542–81554, 2019, doi 10.1109/ACCESS.2019.2923707.
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Published
2023-09-24
How to Cite
hassan, D., & Obied, A. (2023). Different Deep Learning Techniques in Heart Disease Classification: Survey. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(2), Comp Page 63–76. https://doi.org/10.29304/jqcm.2023.15.2.1233
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