Machine Learning Approaches for Heart Disease Detection: A Comprehensive Review
Keywords:
Machine Learning, Classification Techniques, Supervised Learning, Naïve Bayes, Support Vector Machine, Heart Disease, Decision Trees, K- Nearest Neighbor, Random Forest
Abstract
This paper presents a comprehensive review of the application of machine learning algorithms in the early detection of heart disease. Heart disease remains a leading global health concern, necessitating efficient and accurate diagnostic methods. Machine learning has emerged as a promising approach, offering the potential to enhance diagnostic accuracy and reduce the time required for assessments. This review begins by elucidating the fundamentals of machine learning and provides concise explanations of the most prevalent algorithms employed in heart disease detection. It subsequently examines noteworthy research efforts that have harnessed machine learning techniques for heart disease diagnosis. A detailed tabular comparison of these studies is also presented, highlighting the strengths and weaknesses of various algorithms and methodologies. This survey underscores the significant strides made in leveraging machine learning for early heart disease detection and emphasizes the ongoing need for further research to enhance its clinical applicability and efficacy.
References
[1] World Health Organization, "Cardiovascular diseases (CVDs)," [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). [Accessed: September 7, 2023].
[2] American Heart Association, "Heart Disease," [Online]. Available: https://www.heart.org/en/health-topics/heart-disease. [Accessed: September 7, 2023].
[3] Yancy, C. W., Jessup, M., Bozkurt, B., Butler, J., Casey, D. E., Drazner, M. H., ... & Wilkoff, B. L. (2017). 2017 ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation, 136(6), e137-e161.
[4] Saeed, J. N., Abdulazeez, A. M., & Ibrahim, D. A., (2023). Automatic Facial Aesthetic Prediction Based on Deep Learning with Loss Ensembles. Applied Sciences, 13(17), p.9728.
[5] Saeed, J. N., Abdulazeez, A. M. & Ibrahim, D.A., 2023. An Ensemble DCNNs-Based Regression Model for Automatic Facial Beauty Prediction and Analyzation. Traitement du Signal, 40(1), p.55.
[6] Saeed, J. N., Abdulazeez, A. M., & Ibrahim, D. A. (2022, September). 2D Facial Images Attractiveness Assessment Based on Transfer Learning of Deep Convolutional Neural Networks. In 2022 4th International Conference on Advanced Science and Engineering (ICOASE) (pp. 13-18). IEEE.
[7] Saeed, J. N., Abdulazeez, A. M., & Ibrahim, D. A. (2022, September). FIAC-Net: Facial image attractiveness classification based on light deep convolutional neural network. In 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA) (pp. 1-6). IEEE.
[8] Sadeeq, H.T., Ameen, S.Y. and Abdulazeez, A.M., 2022, November. Cancer Diagnosis based on Artificial Intelligence, Machine Learning, and Deep Learning. In 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) (pp. 656-661). IEEE.
[9] Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.
[10] Rajkomar, A., Dean, J., & Kohane, I. (2018). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.
[11] Smith, J. & Johnson, A. (2022). Machine learning algorithms in healthcare: Unveiling latent associations and prognostic insights. Journal of Medical Informatics, 45(2), 123-136.
[12] UCI Machine Learning Repository. (Year). "Cleveland Heart Disease dataset." [Online]. Available: https://archive.ics.uci.edu/ml/datasets/heart+Disease. [Accessed: September 7, 2023].
[13] Alpaydin E., 2010. Introduction to Machine Learning, The MIT Press.
[14] C. M. Bishop, 2006. Pattern Recognition and Machine Learning, Springer.
[15] T. Hastie, R. Tibshirani, & J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.
[16] Liu, B., Liu, C., Zhou, Y., Wang, D., & Dun, Y. (2023). An unsupervised chatter detection method based on AE and merging GMM and K-means. Mechanical Systems and Signal Processing, 186, 109861.
[17] Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.
[18] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
[19] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
[20] Moore, S. K., Schneider, D., & Strickland, E. (2021). How Deep Learning Works: Inside the Neural Networks that Power Today's AI. IEEE Spectrum, 58(10), 32-33.
[21] Zhang, H., Hao, K., Gao, L., Wei, B., & Tang, X. (2022). Optimizing deep neural networks through neuroevolution with stochastic gradient descent. IEEE Transactions on Cognitive and Developmental Systems, 15(1), 111-121.
[22] Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE access, 7, 53040-53065.
[23] Agrawal, P., Abutarboush, H. F., Ganesh, T., & Mohamed, A. W. (2021). Metaheuristic algorithms on feature selection: A survey of one decade of research (2009-2019). IEEE Access, 9, 26766-26791.
[24] Sadeeq, H. T., & Abdulazeez, A. M. (2023). Metaheuristics: A Review of Algorithms. International Journal of Online & Biomedical Engineering, 19(9).
[25] Sadeeq, H. T., & Abdulazeez, A. M. (2023, September). Car side impact design optimization problem using giant trevally optimizer. In Structures (Vol. 55, pp. 39-45). Elsevier.
[26] Sadeeq, H. T., & Abdulazeez, A. M. (2022). Giant trevally optimizer (GTO): A novel metaheuristic algorithm for global optimization and challenging engineering problems. IEEE Access, 10, 121615-121640.
[27] Sadeeq, H. T., & Abdulazeez, A. M. (2022, September). Improved Northern Goshawk Optimization Algorithm for Global Optimization. In 2022 4th International Conference on Advanced Science and Engineering (ICOASE) (pp. 89-94). IEEE.
[28] Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27.
[29] Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.
[30] Ullah, F., Srivastava, G., Xiao, H., Ullah, S., Lin, J. C. W., & Zhao, Y. (2023). A Scalable Federated Learning Approach for Collaborative Smart Healthcare Systems with Intermittent Clients using Medical Imaging. IEEE Journal of Biomedical and Health Informatics.
[31] Yu, X., Qin, W., Lin, X., Shan, Z., Huang, L., Shao, Q., ... & Chen, M. (2023). Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension. Computers in Biology and Medicine, 165, 107408.
[32] Syeds, S., & Thirupathy, P. (2023, May). A novel approach for precision and recall estimation for star rating online customers based on positive movie reviews using naive bayes algorithm over K-nearest neighbour algorithm. In AIP Conference Proceedings (Vol. 2655, No. 1). AIP Publishing.
[33] Akter, S., Siam, A. E., Monir, K. M., Mehedi, M. H. K., & Rasel, A. A. (2023, May). Bengali Movie Recommendation System using K Nearest Neighbor and Cosine Similarity. In Proceedings of the 2023 9th International Conference on Computer Technology Applications (pp. 25-29).
[34] John, G. H., & Langley, P. (2013). Estimating continuous distributions in Bayesian classifiers. arXiv preprint arXiv:1302.4964.
[35] Manning, C. D., Raghavan, P., & Schütze, H. (2008). Xml retrieval. Introduction to Information Retrieval.
[36] Chebil, W., Wedyan, M., Alazab, M., Alturki, R., & Elshaweesh, O. (2023). Improving Semantic Information Retrieval Using Multinomial Naive Bayes Classifier and Bayesian Networks. Information, 14(5), 272.
[37] Yati, J. D., Pamungkas, E. W., Kom, S., & Kom, M. Hate Speech Detection On Social Media Content In Javanese Language With Naive Bayes Algorithm (Doctoral dissertation, Universitas Muhammadiyah Surakarta).
[38] Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees Belmont. CA: Wadsworth International Group.
[39] Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1, 81-106.
[40] Khalili, N., & Rastegar, M. A. (2023). Optimal cost-sensitive credit scoring using a new hybrid performance metric. Expert Systems with Applications, 213, 119232.
[41] Qian, H., Ma, P., Gao, S., & Song, Y. (2023). Soft reordering one-dimensional convolutional neural network for credit scoring. Knowledge-Based Systems, 266, 110414.
[42] Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
[43] Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.
[44] Adugna, T., Xu, W., & Fan, J. (2022). Comparison of random forest and support vector machine classifiers for regional land cover mapping using coarse resolution FY-3C images. Remote Sensing, 14(3), 574
[45] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20, 273-297.
[46] Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.
[47] Xiahou, X., & Harada, Y. (2022). B2C E-commerce customer churn prediction based on K-means and SVM. Journal of Theoretical and Applied Electronic Commerce Research, 17(2), 458-475.
[48] Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7, 81542-81554.
[49] Muppalaneni, N. B., Ma, M., Gurumoorthy, S., Kannan, R., & Vasanthi, V. (2019). Machine learning algorithms with ROC curve for predicting and diagnosing the heart disease. Soft computing and medical bioinformatics, 63-72.
[50] Li, J. P., Haq, A. U., Din, S. U., Khan, J., Khan, A., & Saboor, A. (2020). Heart disease identification method using machine learning classification in e-healthcare. IEEE access, 8, 107562-107582.
[51] Rout, R. K. (2023, May). A Novel Grid Ann for Prediction of Heart Disease. In 2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC) (Vol. 1, pp. 1-6). IEEE.
[52] Gárate-Escamila, A. K., El Hassani, A. H., & Andrès, E. (2020). Classification models for heart disease prediction using feature selection and PCA. Informatics in Medicine Unlocked, 19, 100330.
[53] Muhammad, Y., Tahir, M., Hayat, M., & Chong, K. T. (2020). Early and accurate detection and diagnosis of heart disease using intelligent computational model. Scientific reports, 10(1), 19747.
[54] Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., & Singh, P. (2021). Prediction of heart disease using a combination of machine learning and deep learning. Computational intelligence and neuroscience, 2021.
[55] Kavitha, M., Gnaneswar, G., Dinesh, R., Sai, Y. R., & Suraj, R. S. (2021, January). Heart disease prediction using hybrid machine learning model. In 2021 6th international conference on inventive computation technologies (ICICT) (pp. 1329-1333). IEEE.
[56] Saboor, A., Usman, M., Ali, S., Samad, A., Abrar, M. F., & Ullah, N. (2022). A method for improving prediction of human heart disease using machine learning algorithms. Mobile Information Systems, 2022.
[57] Biswas, N., Ali, M. M., Rahaman, M. A., Islam, M., Mia, M. R., Azam, S., ... & Moni, M. A. (2023). Machine Learning-Based Model to Predict Heart Disease in Early Stage Employing Different Feature Selection Techniques. BioMed Research International, 2023.
[58] Zhou, H., Wang, X., & Zhu, R. (2022). Feature selection based on mutual information with correlation coefficient. Applied Intelligence, 1-18.
[59] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W., & O'Sullivan, J. M. (2022). A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics, 2, 927312.
[60] Kako, N. A., & Abdulazeez, A. M. (2022). Peripapillary Atrophy Segmentation and Classification Methodologies for Glaucoma Image Detection: A Review. Current Medical Imaging, 18(11), 1140-1159
[2] American Heart Association, "Heart Disease," [Online]. Available: https://www.heart.org/en/health-topics/heart-disease. [Accessed: September 7, 2023].
[3] Yancy, C. W., Jessup, M., Bozkurt, B., Butler, J., Casey, D. E., Drazner, M. H., ... & Wilkoff, B. L. (2017). 2017 ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation, 136(6), e137-e161.
[4] Saeed, J. N., Abdulazeez, A. M., & Ibrahim, D. A., (2023). Automatic Facial Aesthetic Prediction Based on Deep Learning with Loss Ensembles. Applied Sciences, 13(17), p.9728.
[5] Saeed, J. N., Abdulazeez, A. M. & Ibrahim, D.A., 2023. An Ensemble DCNNs-Based Regression Model for Automatic Facial Beauty Prediction and Analyzation. Traitement du Signal, 40(1), p.55.
[6] Saeed, J. N., Abdulazeez, A. M., & Ibrahim, D. A. (2022, September). 2D Facial Images Attractiveness Assessment Based on Transfer Learning of Deep Convolutional Neural Networks. In 2022 4th International Conference on Advanced Science and Engineering (ICOASE) (pp. 13-18). IEEE.
[7] Saeed, J. N., Abdulazeez, A. M., & Ibrahim, D. A. (2022, September). FIAC-Net: Facial image attractiveness classification based on light deep convolutional neural network. In 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA) (pp. 1-6). IEEE.
[8] Sadeeq, H.T., Ameen, S.Y. and Abdulazeez, A.M., 2022, November. Cancer Diagnosis based on Artificial Intelligence, Machine Learning, and Deep Learning. In 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) (pp. 656-661). IEEE.
[9] Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.
[10] Rajkomar, A., Dean, J., & Kohane, I. (2018). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.
[11] Smith, J. & Johnson, A. (2022). Machine learning algorithms in healthcare: Unveiling latent associations and prognostic insights. Journal of Medical Informatics, 45(2), 123-136.
[12] UCI Machine Learning Repository. (Year). "Cleveland Heart Disease dataset." [Online]. Available: https://archive.ics.uci.edu/ml/datasets/heart+Disease. [Accessed: September 7, 2023].
[13] Alpaydin E., 2010. Introduction to Machine Learning, The MIT Press.
[14] C. M. Bishop, 2006. Pattern Recognition and Machine Learning, Springer.
[15] T. Hastie, R. Tibshirani, & J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.
[16] Liu, B., Liu, C., Zhou, Y., Wang, D., & Dun, Y. (2023). An unsupervised chatter detection method based on AE and merging GMM and K-means. Mechanical Systems and Signal Processing, 186, 109861.
[17] Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.
[18] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
[19] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
[20] Moore, S. K., Schneider, D., & Strickland, E. (2021). How Deep Learning Works: Inside the Neural Networks that Power Today's AI. IEEE Spectrum, 58(10), 32-33.
[21] Zhang, H., Hao, K., Gao, L., Wei, B., & Tang, X. (2022). Optimizing deep neural networks through neuroevolution with stochastic gradient descent. IEEE Transactions on Cognitive and Developmental Systems, 15(1), 111-121.
[22] Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE access, 7, 53040-53065.
[23] Agrawal, P., Abutarboush, H. F., Ganesh, T., & Mohamed, A. W. (2021). Metaheuristic algorithms on feature selection: A survey of one decade of research (2009-2019). IEEE Access, 9, 26766-26791.
[24] Sadeeq, H. T., & Abdulazeez, A. M. (2023). Metaheuristics: A Review of Algorithms. International Journal of Online & Biomedical Engineering, 19(9).
[25] Sadeeq, H. T., & Abdulazeez, A. M. (2023, September). Car side impact design optimization problem using giant trevally optimizer. In Structures (Vol. 55, pp. 39-45). Elsevier.
[26] Sadeeq, H. T., & Abdulazeez, A. M. (2022). Giant trevally optimizer (GTO): A novel metaheuristic algorithm for global optimization and challenging engineering problems. IEEE Access, 10, 121615-121640.
[27] Sadeeq, H. T., & Abdulazeez, A. M. (2022, September). Improved Northern Goshawk Optimization Algorithm for Global Optimization. In 2022 4th International Conference on Advanced Science and Engineering (ICOASE) (pp. 89-94). IEEE.
[28] Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27.
[29] Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.
[30] Ullah, F., Srivastava, G., Xiao, H., Ullah, S., Lin, J. C. W., & Zhao, Y. (2023). A Scalable Federated Learning Approach for Collaborative Smart Healthcare Systems with Intermittent Clients using Medical Imaging. IEEE Journal of Biomedical and Health Informatics.
[31] Yu, X., Qin, W., Lin, X., Shan, Z., Huang, L., Shao, Q., ... & Chen, M. (2023). Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension. Computers in Biology and Medicine, 165, 107408.
[32] Syeds, S., & Thirupathy, P. (2023, May). A novel approach for precision and recall estimation for star rating online customers based on positive movie reviews using naive bayes algorithm over K-nearest neighbour algorithm. In AIP Conference Proceedings (Vol. 2655, No. 1). AIP Publishing.
[33] Akter, S., Siam, A. E., Monir, K. M., Mehedi, M. H. K., & Rasel, A. A. (2023, May). Bengali Movie Recommendation System using K Nearest Neighbor and Cosine Similarity. In Proceedings of the 2023 9th International Conference on Computer Technology Applications (pp. 25-29).
[34] John, G. H., & Langley, P. (2013). Estimating continuous distributions in Bayesian classifiers. arXiv preprint arXiv:1302.4964.
[35] Manning, C. D., Raghavan, P., & Schütze, H. (2008). Xml retrieval. Introduction to Information Retrieval.
[36] Chebil, W., Wedyan, M., Alazab, M., Alturki, R., & Elshaweesh, O. (2023). Improving Semantic Information Retrieval Using Multinomial Naive Bayes Classifier and Bayesian Networks. Information, 14(5), 272.
[37] Yati, J. D., Pamungkas, E. W., Kom, S., & Kom, M. Hate Speech Detection On Social Media Content In Javanese Language With Naive Bayes Algorithm (Doctoral dissertation, Universitas Muhammadiyah Surakarta).
[38] Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees Belmont. CA: Wadsworth International Group.
[39] Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1, 81-106.
[40] Khalili, N., & Rastegar, M. A. (2023). Optimal cost-sensitive credit scoring using a new hybrid performance metric. Expert Systems with Applications, 213, 119232.
[41] Qian, H., Ma, P., Gao, S., & Song, Y. (2023). Soft reordering one-dimensional convolutional neural network for credit scoring. Knowledge-Based Systems, 266, 110414.
[42] Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
[43] Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.
[44] Adugna, T., Xu, W., & Fan, J. (2022). Comparison of random forest and support vector machine classifiers for regional land cover mapping using coarse resolution FY-3C images. Remote Sensing, 14(3), 574
[45] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20, 273-297.
[46] Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.
[47] Xiahou, X., & Harada, Y. (2022). B2C E-commerce customer churn prediction based on K-means and SVM. Journal of Theoretical and Applied Electronic Commerce Research, 17(2), 458-475.
[48] Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7, 81542-81554.
[49] Muppalaneni, N. B., Ma, M., Gurumoorthy, S., Kannan, R., & Vasanthi, V. (2019). Machine learning algorithms with ROC curve for predicting and diagnosing the heart disease. Soft computing and medical bioinformatics, 63-72.
[50] Li, J. P., Haq, A. U., Din, S. U., Khan, J., Khan, A., & Saboor, A. (2020). Heart disease identification method using machine learning classification in e-healthcare. IEEE access, 8, 107562-107582.
[51] Rout, R. K. (2023, May). A Novel Grid Ann for Prediction of Heart Disease. In 2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC) (Vol. 1, pp. 1-6). IEEE.
[52] Gárate-Escamila, A. K., El Hassani, A. H., & Andrès, E. (2020). Classification models for heart disease prediction using feature selection and PCA. Informatics in Medicine Unlocked, 19, 100330.
[53] Muhammad, Y., Tahir, M., Hayat, M., & Chong, K. T. (2020). Early and accurate detection and diagnosis of heart disease using intelligent computational model. Scientific reports, 10(1), 19747.
[54] Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., & Singh, P. (2021). Prediction of heart disease using a combination of machine learning and deep learning. Computational intelligence and neuroscience, 2021.
[55] Kavitha, M., Gnaneswar, G., Dinesh, R., Sai, Y. R., & Suraj, R. S. (2021, January). Heart disease prediction using hybrid machine learning model. In 2021 6th international conference on inventive computation technologies (ICICT) (pp. 1329-1333). IEEE.
[56] Saboor, A., Usman, M., Ali, S., Samad, A., Abrar, M. F., & Ullah, N. (2022). A method for improving prediction of human heart disease using machine learning algorithms. Mobile Information Systems, 2022.
[57] Biswas, N., Ali, M. M., Rahaman, M. A., Islam, M., Mia, M. R., Azam, S., ... & Moni, M. A. (2023). Machine Learning-Based Model to Predict Heart Disease in Early Stage Employing Different Feature Selection Techniques. BioMed Research International, 2023.
[58] Zhou, H., Wang, X., & Zhu, R. (2022). Feature selection based on mutual information with correlation coefficient. Applied Intelligence, 1-18.
[59] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W., & O'Sullivan, J. M. (2022). A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics, 2, 927312.
[60] Kako, N. A., & Abdulazeez, A. M. (2022). Peripapillary Atrophy Segmentation and Classification Methodologies for Glaucoma Image Detection: A Review. Current Medical Imaging, 18(11), 1140-1159
Published
2023-12-08
How to Cite
A. Taher, H., & M. Abdulazeez, A. (2023). Machine Learning Approaches for Heart Disease Detection: A Comprehensive Review. International Journal of Research and Applied Technology (INJURATECH), 3(2), 267-282. https://doi.org/10.34010/injuratech.v3i2.12052
Section
Articles
