Application of the stacking integrated model in the prediction of breast cancer survival

Yahui Xiao; Lifeng Deng; Xu Zhang; Baohe Song

doi:10.1117/12.3051884

18 November 2024 Application of the stacking integrated model in the prediction of breast cancer survival

Yahui Xiao, Lifeng Deng, Xu Zhang, Baohe Song

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134030K (2024) https://doi.org/10.1117/12.3051884
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Survival data demonstrate a wide range of potential applications in fields such as biomedicine and economics, commonly utilized for prognosticating the presence of illnesses and predicting disease progression, thereby assisting healthcare professionals in formulating personalized diagnoses and treatment plans. In this study, using the METABRIC dataset, we employ Cox's model and random survival forest model to identify a series of influential factors while considering patients' clinical and genetic information. Subsequently, we introduce a stacking integrated model to combine the predictive capabilities of multiple models, resulting in significant optimization of prediction performance compared to individual algorithms. This innovative approach not only enhances understanding of complex disease mechanisms but also provides robust theoretical foundations and empirical evidence for developing more accurate medical prediction models and optimizing patient treatment pathways.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yahui Xiao, Lifeng Deng, Xu Zhang, and Baohe Song "Application of the stacking integrated model in the prediction of breast cancer survival", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134030K (18 November 2024); https://doi.org/10.1117/12.3051884

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