Automatic Data-Driven Classification Systems for Cardiovascular Disease
DOI:
https://doi.org/10.4108/eetpht.10.6430Keywords:
Cardiovascular disease, neural networks, machine learning algorithms, classificationAbstract
Cardiovascular disease (CVD) continues to contribute significantly to preventable deaths and avoidable disability worldwide. Prediction and prevention are of utmost importance in the support of public health. Machine learning and deep learning algorithms have emerged as powerful tools to improve the accuracy of diagnosis, prognosis, and treatment of cardiovascular disease. By employing these technologies, medical professionals can gain valuable insights into the risk factors associated with CVD. The focus of this research is to classify and predict cardiovascular diseases using techniques such as support vector machines, ensemble methods, decision trees, random forests, and neural networks. The effectiveness of these algorithms is evaluated based on metrics including accuracy, sensitivity, specificity, area under the curve (AUC), and F1 score. Results show that support vector machines and ensemble methods offer superior accuracy, while neural networks exhibit higher sensitivity and specificity in predicting cardiovascular diseases.
Downloads
References
Huffman, M. D., Prabhakaran, D., … Sachdev, H. S., & New Delhi Birth Cohort (2011). Incidence of cardiovascular risk factors in an Indian urban cohort results from the New Delhi birth cohort. Journal of the American College of Cardiology, 57(17), 1765–1774. DOI: https://doi.org/10.1016/j.jacc.2010.09.083
https://www.who.int/india/health-topics/cardiovascular-diseases
Kumar, A. S., & Sinha, N. (2020). Cardiovascular disease in India: a 360 degree overview. Medical Journal Armed Forces India, 76(1), 1-3. DOI: https://doi.org/10.1016/j.mjafi.2019.12.005
Wadhawan, S., & Maini, R. (2022). A systematic review on prediction techniques for cardiac disease. International Journal of Information Technologies and Systems Approach (IJITSA), 15(1), 1-33. DOI: https://doi.org/10.4018/IJITSA.290001
Romiti, S., Vinciguerra, M., Saade, W., Anso Cortajarena, I., & Greco, E. (2020). Artificial intelligence (AI) and cardiovascular diseases: an unexpected alliance. Cardiology Research and Practice, 2020. DOI: https://doi.org/10.1155/2020/4972346
Mathur, P., Srivastava, S., Xu, X., & Mehta, J. L. (2020). Artificial intelligence, machine learning, and cardiovascular disease. Clinical Medicine Insights: Cardiology, 14, 1179546820927404. DOI: https://doi.org/10.1177/1179546820927404
Krittanawong, C., Johnson, K. W., Rosenson, R. S., Wang, Z., Aydar, M., Baber, U., ... & Narayan, S. M. (2019). Deep learning for cardiovascular medicine: a practical primer. European heart journal, 40(25), 2058-2073. DOI: https://doi.org/10.1093/eurheartj/ehz056
Princy, R. J. P., Parthasarathy, S., Jose, P. S. H., Lakshminarayanan, A. R., & Jeganathan, S. (2020, May). Prediction of cardiac disease using supervised machine learning algorithms. In 2020 4th international conference on intelligent computing and control systems (ICICCS) (pp. 570-575). IEEE. DOI: https://doi.org/10.1109/ICICCS48265.2020.9121169
Jinjri, W. M., Keikhosrokiani, P., & Abdullah, N. L. (2021, July). Machine learning algorithms for the classification of cardiovascular disease-A comparative study. In 2021 International Conference on Information Technology (ICIT) (pp. 132-138). IEEE. DOI: https://doi.org/10.1109/ICIT52682.2021.9491677
Sharma, S., & Parmar, M. (2020). Heart diseases prediction using deep learning neural network model. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(3), 2244-2248. DOI: https://doi.org/10.35940/ijitee.C9009.019320
Ramprakash, P., Sarumathi, R., Mowriya, R., & Nithyavishnupriya, S. (2020, February). Heart disease prediction using deep neural network. In 2020 International Conference on Inventive Computation Technologies (ICICT) (pp. 666-670). IEEE. DOI: https://doi.org/10.1109/ICICT48043.2020.9112443
https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset
Hasan, N., & Bao, Y. (2021). Comparing different feature selection algorithms for cardiovascular disease prediction. Health and Technology, 11, 49-62. DOI: https://doi.org/10.1007/s12553-020-00499-2
Kanimozhi, G., Shanmugavadivu, P., & Rani, M. M. S. (2020). Machine Learning‐Based Recommender System for Breast Cancer Prognosis. Recommender System with Machine Learning and Artificial Intelligence: Practical Tools and Applications in Medical, Agricultural and Other Industries, 121-140. DOI: https://doi.org/10.1002/9781119711582.ch7
Salama GI, Abdelhalim MB, Zeid MA. Experimental comparison of classifiers for breast cancer diagnosis. In2012 Seventh International Conference on Computer Engineering & Systems (ICCES) 2012 Nov 27 (pp. 180-185). IEEE. DOI: https://doi.org/10.1109/ICCES.2012.6408508
Agarwal A, Saxena A. Malignant Tumor Detection Using Machine Learning through Scikit-learn. International Journal of Pure and Applied Mathematics. 2018;119(15):2863-74.
Rana SP, Dey M, Tiberi G, Sani L, Vispa A, Raspa G, Duranti M, Ghavami M, Dudley S. Machine Learning Approaches for Automated Lesion Detection in Microwave Breast Imaging Clinical Data. Scientific reports. 2019 Jul 19;9(1):1-2. DOI: https://doi.org/10.1038/s41598-019-46974-3
Swathy, M., & Saruladha, K. (2022). A comparative study of classification and prediction of Cardio-vascular diseases (CVD) using Machine Learning and Deep Learning techniques. ICT Express, 8(1), 109-116. DOI: https://doi.org/10.1016/j.icte.2021.08.021
Ganesan, K., Pichai, S., Kavitha, M. S., & Takahashi, M. (2022). Data imputation in deep neural network to enhance breast cancer detection. International Journal of Imaging Systems and Technology, 32(6), 2094-2106. DOI: https://doi.org/10.1002/ima.22743
Naganandhini, Shanmugavadivu, P., Kanimozhi, & Kavitha, M. S. (2021, September). Data imputation of brain MRI features with enhanced multinomial logistic regression for Alzheimer's disease classification. In Proceedings of the 6th International Conference on Sustainable Information Engineering and Technology (pp. 339-347). DOI: https://doi.org/10.1145/3479645.3479676
Ali, L., Rahman, A., Khan, A., Zhou, M., Javeed, A., & Khan, J. A. (2019). An automated diagnostic system for heart disease prediction based on ${chi^{2}} $ statistical model and optimally configured deep neural network. IEEE Access, 7, 34938-34945. DOI: https://doi.org/10.1109/ACCESS.2019.2904800
Khan, A., Qureshi, M., Daniyal, M., & Tawiah, K. (2023). A Novel Study on Machine Learning Algorithm-Based Cardiovascular Disease Prediction. Health & Social Care in the Community, 2023. DOI: https://doi.org/10.1155/2023/1406060
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Muralidharan Jayaraman, Shanmugavadivu Pichai
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
