Open Access
Issue
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
Article Number 01007
Number of page(s) 11
Section Intelligent Computing in Healthcare and Bioinformatics
DOI https://doi.org/10.1051/itmconf/20268401007
Published online 06 April 2026
  1. H. El-Sofany, B. Bouallegue, Y. M. A. El-Latif, A proposed technique for predicting heart disease using machine learning algorithms and an explainable AI method. Sci. Rep. 14, 23277 (2024). https://doi.org/10.1038/s41598-024-74656-2 [Google Scholar]
  2. J. Abdollahi, B. Nouri-Moghaddam, A hybrid method for heart disease diagnosis utilizing feature selection based ensemble classifier model generation. Iran J. Comput. Sci. 5, 229–246 (2022). https://doi.org/10.1007/s42044-022-00104-x [Google Scholar]
  3. M. A. Khan, T. Mazhar, M. M. Yaqoob, M. B. Khan, A. K. J. Saudagar, Y. Y. Ghadi, U. F. Khattak, M. Shahid, Optimal feature selection for heart disease prediction using modified Artificial Bee colony (M-ABC) and K-nearest neighbors (KNN). Sci. Rep. 14, 26241 (2024). https://doi.org/10.1038/s41598-024-78021-1 [Google Scholar]
  4. Z. Zhang, B. Shao, H. Liu, B. Huang, X. Gao, J. Qiu, C. Wang, Construction and Validation of a Predictive Model for Coronary Artery Disease Using Extreme Gradient Boosting. J. Inflamm. Res. 17, 4163–4174 (2024). https://doi.org/10.2147/JIR.S464489 [Google Scholar]
  5. N. G. Rezk, S. Alshathri, A. Sayed, E. El-Din Hemdan, H. El-Behery, XAI-Augmented Voting Ensemble Models for Heart Disease Prediction: A SHAP and LIME-Based Approach. Bioengineering 11, 1016 (2024). https://doi.org/10.3390/bioengineering11101016 [Google Scholar]
  6. C. Tarabanis, E. Kalampokis, M. Khalil, C. L. Alviar, L. A. Chinitz, L. Jankelson, Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction. Cardiovasc. Digit. Health J. 4, 126–132 (2023). https://doi.org/10.1016/j.cvdhj.2023.06.001 [Google Scholar]
  7. H. Luo, C. Xiang, L. Zeng, S. Li, X. Mei, L. Xiong, Y. Liu, C. Wen, Y. Cui, L. Du, Y. Zhou, K. Wang, L. Li, Z. Liu, Q. Wu, J. Pu, R. Yue, SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: Feature selection and model interpretation. Sci. Rep. 14, 17728 (2024). https://doi.org/10.1038/s41598-024-67844-7 [Google Scholar]
  8. S. Sreekumari, R. Bhalla, G. Singh, Feature selection and model evaluation for heart disease prediction using ensemble methods. Procedia Comput. Sci. 259, 1282–1295 (2025). https://doi.org/10.1016/j.procs.2025.04.083 [Google Scholar]
  9. N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002). https://doi.org/10.1613/jair.953 [CrossRef] [Google Scholar]
  10. M. B. Kursa, W. R. Rudnicki, Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010). https://doi.org/10.18637/jss.v036.i11 [CrossRef] [Google Scholar]
  11. 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, 785-794 (2016). https://doi.org/10.1145/2939672.2939785 [Google Scholar]
  12. S. M. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions, in Advances in neural information processing systems 30, 4765–4774 (2017). [Google Scholar]
  13. Q. Fu, Y. Wu, M. Zhu, Y. Xia, Q. Yu, Z. Liu, X. Ma, R. Yang, Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP methodology. Ecotoxicol. Environ. Saf. 286, 117210 (2024). https://doi.org/10.1016/j.ecoenv.2024.117210 [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.