Heart Disease Prediction using NAFS and Image Processing
Keywords:
Heart Disease(HDD), Digital Image Processing(DIP), MATLAB, Machine learning (ML)Abstract
The detection of Heart Disease (HDD) is becoming increasingly important as it is recognised as a leading cause of death worldwide, especially in high-income countries. About 7 million people each year lose their lives to HDD, with men being affected more than women (12%). As a result, several different approaches to disease element analysis have been developed, all with the same goal in mind: to reduce the variation in doctors' clinical practises, as well as clinical costs and mistakes.Developing a robust and clever data-mining-based clinical decision-support system is the primary goal of this study. In order to efficiently detect and identify cardiac illness, the method developed consists of pre-processing, de-noising, clustering, filtering, segmentation, feature extraction, and classification. Other methods such as the Bilateral filter, Gaussian filtering, the average filter, and the notch filter for filtering linked factors, and the Sobel edge detection for locating specific illnesses, are also available. Background and foreground segmentation of identified disease-affected regions is accomplished with the help of ROI segmentation. Extraction of DWT characteristics typically yields a high-quality, relevant image. The New adaptive neuro-fuzzy system (NAFS) algorithm, which is performance-growing when compared to various existing methodologies, was developed in response to this ongoing need for a more sophisticated kind of algorithm. When used to the estimation of the bushy or fuzzy rule sets method in a neural network, this new adaptive neuro-fuzzy system algorithm is a part of the machine learning methodology that is being used to boost the system's overall performance.
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S. Koehler et al., "Unsupervised Domain Adaptation From Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks," in IEEE Transactions on Medical Imaging, vol. 40, no. 10, pp. 2939-2953, Oct. 2021.
V. M. Campello et al., "Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge," in IEEE Transactions on Medical Imaging, vol. 40, no. 12, pp. 3543-3554, Dec. 2021.
H. Peña, S. Gómez, D. Romo-Bucheli and F. Martinez, "Cardiac Disease Representation Conditioned by Spatio-temporal Priors in Cine-MRI Sequences Using Generative Embedding Vectors," 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico, 2021, pp. 5570-5573.
. Deepika, P. B. Srikaanth and R. Pitchai, "Early Detection of Heart Disease Using Deep Learning Model," 2022 8th International Conference on Smart Structures and Systems (ICSSS), Chennai, India, 2022, pp. 1-4.
Y. -M. im, G. -I. Jung, M. -G. Kim, S. -H. Oh, H. -S. Kwon and H. -M. Bae, "Myocardial Attenuation Quantification for Diagnosis of Ischemic Heart Disease," 2022 IEEE International Ultrasonics Symposium (IUS), Venice, Italy, 2022, pp. 1-5.
G. U. Santosh Kumar, T. V. Rajini Kanth, S. V. Raju and S. Malyala, "Advanced Analysis of Cardiac Image Processing Using Hybrid Approach," 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 2021, pp. 1-6.
Pandiaraj, S. L. Prakash and P. R. Kanna, "Effective Heart Disease Prediction Using Hybridmachine Learning," 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, 2021, pp. 731-738.
Q. Lyu et al., "Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network," in IEEE Transactions on Medical Imaging, vol. 40, no. 8, pp. 2170-2181, Aug. 2021.
J. Robert et al., "Spectral Analysis of Tissue Displacement for Cardiac Activation Mapping: Ex Vivo Working Heart and In Vivo Study," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 69, no. 3, pp. 942-956, March 2022.
S. B., S. S., S. S. R., A. R. Nair and M. Raju, "Scalogram Based Heart Disease Classification using Hybrid CNN-Naive Bayes Classifier," 2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), Chennai, India, 2022, pp. 345-348.
Y. Pei et al., "Building a Risk Prediction Model for Postoperative Pulmonary Vein Obstruction via Quantitative Analysis of CTA Images," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 7, pp. 3127-3138, July 2022.
L. P. Koyi, T. Borra and G. L. V. Prasad, "A Research Survey on State of the art Heart Disease Prediction Systems," 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore, India, 2021, pp. 799-806.
R. Raju, N. S. Mokhtar, I. M. Yassin, S. Sangaran, S. N. S. Yasin and S. N. H. Ishak, "Heart Disease Detection using Iridology with ANN," 2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA), Selangor, Malaysia, 2022, pp. 414-418.
M. F. Ihsan, S. Mandala and M. Pramudyo, "Study of Feature Extraction Algorithms on Photoplethysmography (PPG) Signals to Detect Coronary Heart Disease," 2022 International Conference on Data Science and Its Applications (ICoDSA), Bandung, Indonesia, 2022, pp. 300-304.
Banerjee, E. Zacur, R. P. Choudhury and V. Grau, "Automated 3D Whole-Heart Mesh Reconstruction From 2D Cine MR Slices Using Statistical Shape Model," 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom, 2022, pp. 1702-1706.
G. Zamzmi, L. -Y. Hsu, W. Li, V. Sachdev and S. Antani, "Harnessing Machine Intelligence in Automatic Echocardiogram Analysis: Current Status, Limitations, and Future Directions," in IEEE Reviews in Biomedical Engineering, vol. 14, pp. 181-203, 2021.
K. Mehta and K. Subramanian, "Heart Disease Diagnosis using Deep Learning," 2022 IEEE India Council International Subsections Conference (INDISCON), Bhubaneswar, India, 2022, pp. 1-6.
Sharma, R. Kumar and V. Jaiswal, "Classification of Heart Disease from MRI Images Using Convolutional Neural Network," 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India, 2021, pp. 358-363.
M. Ramesh, S. Mandapati, B. V. V. S. Prasad and B. S. Kumar, "Machine Learning based Cardiac Magnetic Resonance Imaging (CMRI) for Cardiac Disease Detection," 2021 Second International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), Bengaluru, India, 2021, pp. 1-5.
T. P. Naidu et al., "A Hybridized Model for the Prediction of Heart Disease using ML Algorithms," 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2021, pp. 256-261.
M. Kavitha, G. Gnaneswar, R. Dinesh, Y. R. Sai and R. S. Suraj, "Heart Disease Prediction using Hybrid machine Learning Model," 2021 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2021, pp. 1329-1333.
V. R. Elangovan, A. J. R. Joe, D. Akila, K. H. Shankari and G. Suseendran, "Heart atherosclerosis detection using FCM+kMeans Algorithm," 2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates, 2021, pp. 102-106.
S. K. L. Sameer and P. Sriramya, "Improving the Accuracy for Prediction of Heart Disease by Novel Feature Selection Scheme using Decision tree comparing with Naive-Bayes Classifier Algorithms," 2022 International Conference on Business Analytics for Technology and Security (ICBATS), Dubai, United Arab Emirates, 2022, pp. 1-8.
L. Mugambi, L. ZÜHLKE and C. W. Maina, "Towards AI Based Diagnosis of Rheumatic Heart Disease: Data Annotation and View Classification," 2022 IST-Africa Conference (IST-Africa), Ireland, 2022, pp. 1-8.
W. Wang et al., "Few-Shot Learning by a Cascaded Framework With Shape-Constrained Pseudo Label Assessment for Whole Heart Segmentation," in IEEE Transactions on Medical Imaging, vol. 40, no. 10, pp. 2629-2641, Oct. 2021.
M. Jacobs et al., "Automated Segmental Analysis of Fully Quantitative Myocardial Blood Flow Maps by First-Pass Perfusion Cardiovascular Magnetic Resonance," in IEEE Access, vol. 9, pp. 52796-52811, 2021.
S. Qiao, S. Pang, G. Luo, S. Pan, T. Chen and Z. Lv, "FLDS: An Intelligent Feature Learning Detection System for Visualizing Medical Images Supporting Fetal Four-Chamber Views," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 10, pp. 4814-4825, Oct. 2022.
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