Machine Learning and Deep Leaning in Predicting Coronary Heart Disease
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-60935-0_10
Reference13 articles.
1. Rafiq, M., et al.: Competing endogenous RNA regulatory networks of hsa_circ_0126672 in pathophysiology of coronary heart disease. Genes 14(3), 550 (2023). https://doi.org/10.3390/genes14030550
2. Wang, J., Rao, C., Goh, M., Xiao, X.: Risk assessment of coronary heart disease based on cloud-random forest. Artif. Intell. Rev. 56(1), 203–232 (2023). https://doi.org/10.1007/s10462-022-10170-z
3. Yilmaz, R., Yağın, F.H.: Early detection of coronary heart disease based on machine learning methods. Med. Rec. 4(1), 1–6 (2022). https://doi.org/10.37990/medr.1011924
4. Chen, X., Fu, Y., Lin, J., Ji, Y., Fang, Y., Wu, J.: Coronary artery disease detection by machine learning with coronary bifurcation features. Appl. Sci. 10(21), 7656 (2020). https://doi.org/10.3390/app10217656
5. Bertsimas, D., Orfanoudaki, A., Weiner, R.B.: Personalized treatment for coronary artery disease patients: a machine learning approach. Health Care Manag. Sci. 23, 482–506 (2020). https://doi.org/10.1007/s10729-020-09522-4
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