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@article{174650,
        author = {Ms. Sinduja  S and Dr.Mohanapriya  N and Rahini   N and Nithyasree RS and Yazhini R},
        title = {Predicting  Liver Cancer Staging Using Ensemble Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {689-695},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174650},
        abstract = {Prediction from the Liver Cancer Conference (PLCS), an important indicator for evaluating the development of primary liver cancer cells (PLCCS), is extremely important in the diagnosis, treatment and rehabilitation of liver cancer. This project introduces an integrated system that integrates CNN, KNN, and Gemini AI to improve liver cancer prediction and control.
This system is based on the CNN model of CT-SCAN via the CNN model that predicts stadiums (0, A, B, C, D) based on the BCLC staging system. The KNN model checked and fine-tuned predictions by examining information on clinical and radiation reports based on the BCLC guidelines. Gemini AI recommends patient-specific treatment schemes to optimize patient care with the potential for resection, ablation, implantation, and systemic therapy.
Additionally, the web-based portal offers health instructions and prefabricated YouTube videos for patient training and access to resources. The combination of CNN, KNN, and Gemini AI improves classification accuracy and treatment suggestions, while the interactive portal increases patient awareness and participation. Experimental results demonstrate the effectiveness of the system in planning and treatment planning for liver cancer, making it a useful tool for clinicians and improving patient outcome.},
        keywords = {},
        month = {March},
        }