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

Autor: Ejiyi CJ; School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China., Qin Z; School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China., Monday H; Department of Computer Science, Oxford Brookes University and Chengdu University of Technology of China, Chengdu, China., Ejiyi MB; Department of Pharmacy, University of Nigeria Nsukka, Enugu, Nigeria., Ukwuoma C; School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China., Ejiyi TU; Department of Pure and Industrial Chemistry, University of Nigeria Nsukka, Enugu, Nigeria., Agbesi VK; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China., Agu A; Department of Public Health, University of Nigeria Enugu Campus, Enugu, Nigeria., Orakwue C; Department of Agricultural and Bio-Resources Engineering, College of Engineering Federal University of Agriculture Abeokuta, Nigeria.
Jazyk: angličtina
Zdroj: BioFactors (Oxford, England) [Biofactors] 2024 Jan-Feb; Vol. 50 (1), pp. 114-134. Date of Electronic Publication: 2023 Sep 11.
DOI: 10.1002/biof.1995
Abstrakt: Recent 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 focus on providing quality care to ensure the best possible outcomes for patients. The need for proper management of the disease state is crucial so as to reduce the recurrence of the disease and other associated complications. Thus the interpretability provided by SHAP enlightens the management strategies in this study focusing on the quality of care given to the patient and how timely the care is.
(© 2023 International Union of Biochemistry and Molecular Biology.)
Databáze: MEDLINE
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