Browsing by Author "MEHREEN ILYAS"

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    A Proposed Framework for the prediction of Breast cancer by using Federated Learning
    (UMT, Lahore, 2024) MEHREEN ILYAS
    The leading cause of death for women is breast cancer. Although genetic factors substantially assist in the growth of breast cancer, recent studies show that environmental factors are also essential in the occurrence and spread of the disease. The escalation of environmental factors has become a noteworthy worldwide concern that carries substantial consequences for human health, specifically in connection with breast cancer, resulting in a rise in the incidence and intensity of breast cancer. This study aims to assess Federated Learning's predictive accuracy for breast cancer. Several machine learning techniques, such as XG Boost, Random Forest, Support Vector Classifier, Artificial Neural Network, and stacking classifier, have been studied by researchers to forecast breast cancer issues. Facilitating local data collecting and analysis while maintaining privacy and eliminating the need for centralized data aggregation is one of FL's competitive advantages. Given its capacity to evaluate a variety of locally stored data without jeopardizing patient privacy, FL is the suggested approach for breast cancer prediction. The unique features of FL include privacy protection, local data collecting and analysis, and the removal of the requirement for a centralized data repository.