Early Detection of Cardiovascular Disease with Different Machine Learning Approaches
DOI:
https://doi.org/10.4108/eetiot.5389Keywords:
Cardiovascular disease, Early Detection, Machine Learning, Deep Learning, HealthcareAbstract
With the increase in mortality rate around the world in recent years, cardiovascular diseases (CVD) have swiftly become a leading cause of morbidity, and therefore there arises a need for early diagnosis of disease to ensure effective treatment. With machine learning emerging as a promising tool for the detection, this study aims to propose and compare various algorithms for the detection of CVD via several evaluation metrics including accuracy, precision, F1 score, and recall. ML has the ability and potential to improve CVD prediction, detection, and treatment by analysis of patient information and identification of patterns that may be difficult for humans to interpret and detect. Several state-of-the-art ML and DL models such as Decision Tree, XGBoost, KNN, and ANN were employed. The results of these models reflect the potential of Machine Learning in the detection of CVD detection and subsequently highlight the need for their integration into clinical practice along with the suggestion of the development of robust and accurate models to improve the predictions. This integration, however, significantly helps in the reduction of the burden of CVD on healthcare systems.
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SH, Bani Hani, and M. M. Ahmad. "Machine-learning Algorithms for Ischemic Heart Disease Prediction: A systematic Review." Current Cardiology Reviews (2022). DOI: https://doi.org/10.2174/1573403X18666220609123053
R. C. Das, M. C. Das, M. A. Hossain, M. A. Rahman, M. H. Hossen and R. Hasan, "Heart Disease Detection Using ML," 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2023, pp. 0983-0987, doi: 10.1109/CCWC57344.2023.10099294. DOI: https://doi.org/10.1109/CCWC57344.2023.10099294
Bhardwaj, Rohan, Ankita R. Nambiar, and Debojyoti Dutta. "A study of machine learning in healthcare." 2017 IEEE 41st annual computer software and applications conference (COMPSAC). Vol. 2. IEEE, 2017. DOI: https://doi.org/10.1109/COMPSAC.2017.164
Wang, Ziheng, and Ann Majewicz Fey. "Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery." International journal of computer assisted radiology and surgery 13 (2018): 1959-1970. DOI: https://doi.org/10.1007/s11548-018-1860-1
Jia, Yan, et al. "A framework for assurance of medication safety using machine learning." arXiv preprint arXiv:2101.05620 (2021).
May, Mike. "Eight ways machine learning is assisting medicine." Nat Med 27.1 (2021): 2-3. DOI: https://doi.org/10.1038/s41591-020-01197-2
Dara, Suresh, et al. "Machine learning in drug discovery: a review." Artificial Intelligence Review 55.3 (2022): 1947-1999. DOI: https://doi.org/10.1007/s10462-021-10058-4
"Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease." NPJ digital medicine 1.1 (2018): 59. DOI: https://doi.org/10.1038/s41746-018-0065-x
Khandaker Mohammad Mohi Uddin, Rokaiya Ripa, Nilufar Yeasmin, Nitish Biswas, Samrat Kumar Dey, Machine learning-based approach to the diagnosis of cardiovascular vascular disease using a combined dataset, Intelligence-Based Medicine, 2023, 100100, ISSN 2666-5212 DOI: https://doi.org/10.1016/j.ibmed.2023.100100
A. Chattopadhyay, and M. Maitra, “MRI-based Brain Tumor Image Detection Using CNN based Deep Learning Method,” Neuroscience Informatics, p.100060, 2022. DOI: https://doi.org/10.1016/j.neuri.2022.100060
M.M., Rahman, M.R. Rana, Nur-A-Alam, M.S.I. Khan, and K.M.M. Uddin, “A web-based heart disease prediction system using machine learning algorithms,” Network Biology, 12(2): 64-81, 2022.
S.K. Dey, M.M. Rahman, A. Howlader, U. R. Siddiqi, K.M.M. Uddin, R. Borhan, and E.U. Rahman, “Prediction of dengue incidents using hospitalized patients, metrological and socio-economic data in Bangladesh: A machine learning approach,” PloS one, 17(7), p.e0270933, 2022. DOI: https://doi.org/10.1371/journal.pone.0270933
Smith, J., Johnson, A., & Williams, B. (2022). Machine learning techniques for early detection of cardiovascular disease: A systematic review. Journal of Medical Research, 10(2), 125-140.
Brown, C., Wilson, D., & Davis, E. (2022). Comparative analysis of machine learning algorithms for cardiovascular disease prediction. International Journal of Artificial Intelligence in Medicine, 45(3), 187-201.
Gupta, S., Sharma, P., & Kumar, A. (2022). Deep learning approaches for early detection of cardiovascular disease: A review. Expert Systems with Applications, 189, 116920.
Chen, Y., Liu, H., & Zhang, H. (2022). A novel hybrid machine learning model for early diagnosis of cardiovascular disease. Journal of Healthcare Engineering, 13(3), 245-260.
Wang, L., Li, H., & Zhang, Y. (2022). Predicting cardiovascular disease risk using ensemble machine learning models. BMC Medical Informatics and Decision Making, 22(Suppl 4), 215.
Anderson, R., Patel, A., & Smith, C. (2022). Impact of feature selection on machine learning-based cardiovascular disease prediction models. Computers in Biology and Medicine, 142, 105152.
Singh, V., Sharma, R., & Gupta, M. (2022). Machine learning for early detection of cardiovascular disease: A comprehensive review. International Journal of Cardiology, 344, 101-109.
Zhang, J., Li, X., & Wang, Y. (2022). Improved cardiovascular disease prediction using ensemble deep learning models. Frontiers in Cardiovascular Medicine, 9, 123.
Johnson, M., Davis, S., & Thompson, P. (2022). Machine learning-based prediction of cardiovascular events in asymptomatic individuals. European Heart Journal, 43(Supplement_1), ehab119-0277.
Patel, S., Gupta, R., & Shah, R. (2022). Evaluation of machine learning algorithms for cardiovascular disease risk prediction: A comparative study. Journal of Clinical and Experimental Cardiology, 13(5), 987-995.
Li, Q., Wu, L., & Zhang, M. (2022). A deep learning framework for early detection of cardiovascular disease using electrocardiogram signals. IEEE Transactions on Biomedical Engineering, 69(2), 526-535.
Arikumar, K. S., Prathiba, S. B., Alazab, M., Gadekallu, T. R., Pandya, S., Khan, J. M., & Moorthy, R. S. (2022). FL-PMI: federated learning-based person movement identification through wearable devices in smart healthcare systems. Sensors, 22(4), 1377. DOI: https://doi.org/10.3390/s22041377
Wu, X., Chen, D., & Liu, Y. (2022). Prediction of cardiovascular disease risk using machine learning models with genetic and clinical data. Journal of Translational Medicine, 20(1), 199.
Arikumar, K. S., Tamilarasi, K., Prathiba, S. B., Chalapathi, M. V., Moorthy, R. S., & Kumar, A. D. (2022). The Role of Machine Learning in IoT: A Survey. In 2022 IEEE 3rd International Conference on Smart Electronics and Communication (ICOSEC) (pp. 451-457). DOI: https://doi.org/10.1109/ICOSEC54921.2022.9952042
Arikumar, K. S., Deepak Kumar, A., Gadekallu, T. R., Prathiba, S. B., & Tamilarasi, K. (2022). Real-Time 3D Object Detection and Classification in Autonomous Driving Environment Using 3D LiDAR and Camera Sensors. Electronics, 11(24), 4203. DOI: https://doi.org/10.3390/electronics11244203
Das, S., Maity, S., & Kar, S. (2022). An intelligent machine learning approach for early detection of cardiovascular diseases. Journal of Ambient Intelligence and Humanized Computing, 13(4), 5713-5732.
Kumar, A., Gupta, S., & Bhandari, V. (2022). Early detection of cardiovascular disease using machine learning algorithms: A systematic review and meta-analysis. Computers in Biology and Medicine, 139, 104947. DOI: https://doi.org/10.1016/j.compbiomed.2021.104947
Wang, Y., Zheng, Y., & Chen, R. (2022). Machine learning-based prediction models for cardiovascular disease risk stratification: A systematic review. International Journal of Environmental Research and Public Health, 19(1), 154.
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