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@article{167837,
        author = {Chittinni Pratyusha and G. Praveen Babu},
        title = {Analysis of Utility Kernal Based SVM for survival Estimation of Breast Cancer},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {4},
        pages = {477-482},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167837},
        abstract = {The advancement of medical research in cancer prognosis and diagnosis, particularly in breast cancer, has placed significant stress on oncologists due to the complexity and heterogeneity of the disease. To address this challenge, research focusing on breast cancer survival estimation has been proposed. This research aims to merge histological and genomic data to enhance prognosis accuracy, minimizing unnecessary treatment interventions and ensuring tailored patient care. By utilizing a spectrum of machine learning approaches, including SVM, Random Forest, and neural networks, a robust tool is developed for personalized breast cancer survival predictions. The tool empowers clinicians to make informed treatment decisions and optimize healthcare resource allocation efficiently. Moreover, the application of ensemble methods further enhances prediction accuracy, particularly through techniques like Voting Classifier. Additionally, extending the project to include a user-friendly frontend using the Flask framework allows for user testing and authentication, facilitating seamless interaction and practical application in clinical settings.},
        keywords = {Breast cancer survival estimation, gene expression, copy number variation, histopathological whole slide images, utility kernel, support vector machine, machine learning, deep neural networks},
        month = {September},
        }