Open Access
Issue
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
Article Number 04023
Number of page(s) 8
Section AI and Advanced Applications
DOI https://doi.org/10.1051/itmconf/20257004023
Published online 23 January 2025
  1. World Health Organisation. Noncommunicable diseases [updated 11 June, 2021; cited 2024 6 October,]. Available from: https://www.who.int/zh/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) [Google Scholar]
  2. S.S. Martin, A.W. Aday, Z.I. Almarzooq, C.A.M. Anderson, P. Arora, C.L. Avery, American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee, 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation 149, 8 (2024). [Google Scholar]
  3. W. Li, S. Lin, Y. He, J. Wang, Y. Pan, Deep learning survival model for colorectal cancer patients (DeepCRC) with Asian clinical data compared with different theories. Arch. Med. Sci. 19, 264-269 (2023). [CrossRef] [Google Scholar]
  4. J.-O. Jung, N. Crnovrsanin, N.M. Wirsik, H. Nienhuser, L. Peters, F. Popp, et al., Machine learning for optimized individual survival prediction in resectable upper gastrointestinal cancer. J. Cancer Res. Clin. Oncol. 149, 1691-1702 (2023). [CrossRef] [Google Scholar]
  5. P. Doupe, J. Faghmous, S. Basu, Machine Learning for Health Services Researchers. Health 22, 808-815 (2019). [Google Scholar]
  6. M. Mohammed, M.B. Khan, E.B.M. Bashier, Machine learning: algorithms and applications. CRC Press (2016). [Google Scholar]
  7. S. Bashir, U. Qamar, F.H. Khan, A multicriteria weighted vote-based classifier ensemble for heart disease prediction. Comput. Intell. 32, 615-645 (2016). [CrossRef] [MathSciNet] [Google Scholar]
  8. A. Dutta, et al., An efficient convolutional neural network for coronary heart disease prediction. Expert Syst. Appl. 159, 113408 (2020). [CrossRef] [Google Scholar]
  9. V. Krishnaiah, G. Narsimha, N.S. Chandra, Emerging ICT for Bridging the Future—Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1 (2015). [Google Scholar]
  10. Heart Disease Cleveland UCI, Kaggle (2019). https://www.kaggle.com/datasets/cherngs/heart-disease-cleveland-uci/code [Google Scholar]
  11. R. Katarya, S.K. Meena, Machine Learning Techniques for Heart Disease Prediction: A Comparative Study and Analysis. Health Technol. 11, 87-97 (2021). [CrossRef] [Google Scholar]
  12. P.-N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining. Addison-Wesley Longman Publishing Co., Inc. (2005). [Google Scholar]
  13. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20, 273-297 (1995). [Google Scholar]
  14. H. Taud, J.-F. Mas, Multilayer perceptron (MLP). In: Geomatic Approaches for Modeling Land Change Scenarios, pp. 451–455 (2018). [CrossRef] [Google Scholar]
  15. T. Chen, C. Guestrin, Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016). [Google Scholar]
  16. B. Pavlyshenko, Using stacking approaches for machine learning models. In: 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), IEEE (2018). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.