References
[1]. Alpaydin, E. (2010). Introduction to machine learning (2nd ed.). Cambridge, MA: MIT Press.
[2]. Murphy, K. P. (2012). Machine learning: a probabilistic perspective. Cambridge, MA: MIT Press.
[3]. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed.). New York, NY: Springer.
[4]. Tabak, Y. P., Sun, X., Johannes, R. S., & Nunez, C. M. (2019). Machine learning for early detection of heart failure hospitalization. BMC medical informatics and decision making, 19(1), 44.
[5]. Chen, Y., Liang, X., Peng, Y., Li, Z., & Zhang, J. (2020). Real-time risk prediction for heart failure based on wearable sensors and electronic health records. BMC medical informatics and decision making, 20(1), 49.
[6]. Wang, Z., Chen, Y., & Gong, J. (2020). Heart failure prediction based on SVM classification model. Journal of Healthcare Engineering, 2020, 1-8.
[7]. Lin, Y. H., Liu, Y. F., Chang, K. C., & Yang, Y. C. (2021). Heart Failure Prediction with Boosting Techniques and Feature Selection Algorithms. Healthcare, 9(4), 428.
[8]. Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Oakland, CA: Analytics Press.
[9]. Heer, J., & Shneiderman, B. (2012). Interactive dynamics for visual analysis. Communications of the ACM, 55(4), 45-54.
[10]. Johnson, C. R., Peterson, J. B., & Summers, R. M. (2017). Interactive visualizations for medical big data. Journal of biomedical informatics, 71, 1-4.
[11]. Liu, F., & Mehra, M. R. (2019). Data Visualization in Heart Failure. Journal of cardiac failure, 25(8), 635-639.
[12]. Tufte, E. R. (2001). The visual display of quantitative information. Graphics press.
[13]. Keim, D., Mansmann, F., Schneidewind, J., & Ziegler, H. (2008). Challenges in visual data analysis. In Visual data mining (pp. 1-34). Springer.
[14]. Kosara, R., Miksch, S., & Hauser, H. (2003). Interactive information visualization for exploring and querying electronic health records: A systematic review. Foundations and Trends® in Human–Computer Interaction, 1(2), 91-155.
[15]. Satyanarayan, A., Moritz, D., Wongsuphasawat, K., & Heer, J. (2016). Vega-lite: A grammar of interactive graphics. IEEE transactions on visualization and computer graphics, 23(1), 341-350.
[16]. Chen, C., & Zhang, C. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.
[17]. Brehmer, M., & Munzner, T. (2013). A multi-level typology of abstract visualization tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2376-2385.
[18]. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
[19]. Breiman, L. (2017). Classification and regression trees. Routledge.
[20]. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
[21]. Nguyen, D., Cios, K. J., & Wozniak, M. (2019). Data mining methods in the prediction of Drosophila melanogaster gene expression. IEEE Transactions on Evolutionary Computation, 23(3), 546-560.
Cite this article
Zhou,Z. (2023). Application analysis of machine learning and data visualization in heart failure prediction. Applied and Computational Engineering,20,181-188.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Alpaydin, E. (2010). Introduction to machine learning (2nd ed.). Cambridge, MA: MIT Press.
[2]. Murphy, K. P. (2012). Machine learning: a probabilistic perspective. Cambridge, MA: MIT Press.
[3]. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed.). New York, NY: Springer.
[4]. Tabak, Y. P., Sun, X., Johannes, R. S., & Nunez, C. M. (2019). Machine learning for early detection of heart failure hospitalization. BMC medical informatics and decision making, 19(1), 44.
[5]. Chen, Y., Liang, X., Peng, Y., Li, Z., & Zhang, J. (2020). Real-time risk prediction for heart failure based on wearable sensors and electronic health records. BMC medical informatics and decision making, 20(1), 49.
[6]. Wang, Z., Chen, Y., & Gong, J. (2020). Heart failure prediction based on SVM classification model. Journal of Healthcare Engineering, 2020, 1-8.
[7]. Lin, Y. H., Liu, Y. F., Chang, K. C., & Yang, Y. C. (2021). Heart Failure Prediction with Boosting Techniques and Feature Selection Algorithms. Healthcare, 9(4), 428.
[8]. Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Oakland, CA: Analytics Press.
[9]. Heer, J., & Shneiderman, B. (2012). Interactive dynamics for visual analysis. Communications of the ACM, 55(4), 45-54.
[10]. Johnson, C. R., Peterson, J. B., & Summers, R. M. (2017). Interactive visualizations for medical big data. Journal of biomedical informatics, 71, 1-4.
[11]. Liu, F., & Mehra, M. R. (2019). Data Visualization in Heart Failure. Journal of cardiac failure, 25(8), 635-639.
[12]. Tufte, E. R. (2001). The visual display of quantitative information. Graphics press.
[13]. Keim, D., Mansmann, F., Schneidewind, J., & Ziegler, H. (2008). Challenges in visual data analysis. In Visual data mining (pp. 1-34). Springer.
[14]. Kosara, R., Miksch, S., & Hauser, H. (2003). Interactive information visualization for exploring and querying electronic health records: A systematic review. Foundations and Trends® in Human–Computer Interaction, 1(2), 91-155.
[15]. Satyanarayan, A., Moritz, D., Wongsuphasawat, K., & Heer, J. (2016). Vega-lite: A grammar of interactive graphics. IEEE transactions on visualization and computer graphics, 23(1), 341-350.
[16]. Chen, C., & Zhang, C. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.
[17]. Brehmer, M., & Munzner, T. (2013). A multi-level typology of abstract visualization tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2376-2385.
[18]. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
[19]. Breiman, L. (2017). Classification and regression trees. Routledge.
[20]. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
[21]. Nguyen, D., Cios, K. J., & Wozniak, M. (2019). Data mining methods in the prediction of Drosophila melanogaster gene expression. IEEE Transactions on Evolutionary Computation, 23(3), 546-560.