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Predictive Modeling and Personalized Care: MachineLearning's Impact on Cardiovascular Disease (CVD) Prevention
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
Global burden of cardiovascular disease (CVD) accounts for high mortality and developing new intervention strategies for early detection, risk assessment, and individualized management. Machine learning is a revolutionary technology in healthcare and is capable to discover new patterns from complex datasets in large amount than other traditional approaches. Continuing this article, the subject area is going to be defined in what extent machine learning supports the prevention of CVD through the application of predictive modeling, personalized treatments and constant supervision and monitoring. With the help of ML algorithms, clinicians will be able to forecast cardiovascular events, individualize patient care treatment, and track a patient?s state with the help of wearable devices. The introduction of ML in clinical practice enhances both the probabilities of risk assessment and provides better strategies of prevention compared to the conventional methods, hence a giant stride on the fight against CVD.
Keywords
Cardiovascular disease prevention, Machine learning, Predictive modeling, risk stratification, personalized treatment, Healthcare technology, Artificial intelligence, Wearable health monitoring, Predictive analytics.
References
[1] Weng, S., Reps, J., Kai, J., Garibaldi, J., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data?. PLoS ONE, 12. https://doi.org/10.1371/journal.pone.0174944.
[2] Krittanawong, C., Krittanawong, C., Virk, H., Bangalore, S., Wang, Z., Wang, Z., Johnson, K., Pinotti, R., Zhang, H., Kaplin, S., Narasimhan, B., Kitai, T., Baber, U., Halperin, J., & Tang, W. (2020). Machine learning prediction in cardiovascular diseases: a meta-analysis. Scientific Reports, 10. https://doi.org/10.1038/s41598-020-72685-1.
[3] Cho, S., Kim, S., Kang, S., Lee, K., Choi, D., Kang, S., Park, S., Kim, T., Yoon, C., Youn, T., & Chae, I. (2021). Pre-existing and machine learning-based models for cardiovascular risk prediction. Scientific Reports, 11. https://doi.org/10.1038/s41598-021-88257-w.
[4] Xi, Y., Wang, H., & Sun, N. (2022). Machine learning outperforms traditional logistic regression and offers new possibilities for cardiovascular risk prediction: A study involving 143,043 Chinese patients with hypertension. Frontiers in Cardiovascular Medicine, 9. https://doi.org/10.3389/fcvm.2022.1025705.
[5] Nadakinamani, R., Reyana, A., Kautish, S., Vibith, A., Gupta, Y., Abdelwahab, S., & Mohamed, A. (2022). Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/2973324.
[6] Dalal, S., Goel, P., Onyema, E., Alharbi, A., Mahmoud, A., Algarni, M., & Awal, H. (2023). Application of Machine Learning for Cardiovascular Disease Risk Prediction. Computational Intelligence andNeuroscience. https://doi.org/10.1155/2023/9418666.
[7] El-Hasnony, I., Elzeki, O., Alshehri, A., & Salem, H. (2022). Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction. Sensors (Basel, Switzerland), 22. https://doi.org/10.3390/s22031184.
[8] Subramani, S., Varshney, N., Anand, M., Soudagar, M., Al-Keridis, L., Upadhyay, T., Alshammari, N., Saeed, M., Subramanian, K., Anbarasu, K., & Rohini, K. (2023). Cardiovascular diseases prediction by machine learning incorporation with deep learning. Frontiers in Medicine, 10.https://doi.org/10.3389/fmed.2023.1150933
How to cite this paper
@article{1704946,
author = {Anshul Khairari, Priyabrata Thatoi, Sushree Swapnil Rout, Pooja Patil},
title = {Predictive Modeling and Personalized Care: MachineLearning's Impact on Cardiovascular Disease (CVD) Prevention},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {7},
number = {2},
pages = {683-689},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/17049461.pdf},
abstract = {Global burden of cardiovascular disease (CVD) accounts for high mortality and developing new intervention strategies for early detection, risk assessment, and individualized management. Machine learning is a revolutionary technology in healthcare and is capable to discover new patterns from complex datasets in large amount than other traditional approaches. Continuing this article, the subject area is going to be defined in what extent machine learning supports the prevention of CVD through the application of predictive modeling, personalized treatments and constant supervision and monitoring. With the help of ML algorithms, clinicians will be able to forecast cardiovascular events, individualize patient care treatment, and track a patient?s state with the help of wearable devices. The introduction of ML in clinical practice enhances both the probabilities of risk assessment and provides better strategies of prevention compared to the conventional methods, hence a giant stride on the fight against CVD.},
keywords = {Cardiovascular disease prevention, Machine learning, Predictive modeling, risk stratification, personalized treatment, Healthcare technology, Artificial intelligence, Wearable health monitoring, Predictive analytics.},
month = {August},
}