Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation

The use of SHAP for feature engineering alongside random augmentation for data balancing for the diagnosis of breast cancer (BC) proposed some management strategies for BC before and after diagnosis Developed models that could help in the diagnosis of BC by prediction using various machine learning algorithms. AbstractRecent research indicates that early detection of breast cancer (BC) is critical in achieving favorable treatment outcomes and reducing the mortality rate associated with it. With the difficulty in obtaining a balanced dataset that is primarily sourced for the diagnosis of the disease, many researchers have relied on data augmentation techniques, thereby having varying datasets with varying quality and results. The dataset we focused on in this study is crafted from SHapley Additive exPlanations (SHAP)-augmentation and random augmentation (RA) approaches to dealing with imbalanced data. This was carried out on the Wisconsin BC dataset and the effectiveness of this approach to the diagnosis of BC was checked using six machine-learning algorithms. RA synthetically generated some parts of the dataset while SHAP helped in assessing the quality of the attributes, which were selected and used for the training of the models. The result from our analysis shows that the performance of the models used generally increased to more than 3% for most of the models using the dataset obtained by the integration of SHAP and RA. Additionally, after diagnosis, it is important to fo...
Source: BioFactors - Category: Biochemistry Authors: Tags: RESEARCH ARTICLE Source Type: research