Intelligent breast cancer diagnostic system empowered by deep extreme gradient descent optimizationopen access
- Authors
- Khan, Muhammad Bilal Shoaib; Atta-ur-Rahman; Nawaz, Muhammad Saqib; Ahmed, Rashad; Khan, Muhammad Adnan; Mosavi, Amir
- Issue Date
- May-2022
- Publisher
- AMER INST MATHEMATICAL SCIENCES-AIMS
- Keywords
- machine learning; deep extreme; gradient descent optimization; random forest; decision tree; artificial intelligence; breast cancer
- Citation
- MATHEMATICAL BIOSCIENCES AND ENGINEERING, v.19, no.8, pp.7978 - 8002
- Journal Title
- MATHEMATICAL BIOSCIENCES AND ENGINEERING
- Volume
- 19
- Number
- 8
- Start Page
- 7978
- End Page
- 8002
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/85037
- DOI
- 10.3934/mbe.2022373
- ISSN
- 1547-1063
- Abstract
- Cancer is a manifestation of disorders caused by the changes in the body's cells that go far beyond healthy development as well as stabilization. Breast cancer is a common disease. According to the stats given by the World Health Organization (WHO), 7.8 million women are diagnosed with breast cancer. Breast cancer is the name of the malignant tumor which is normally developed by the cells in the breast. Machine learning (ML) approaches, on the other hand, provide a variety of probabilistic and statistical ways for intelligent systems to learn from prior experiences to recognize patterns in a dataset that can be used, in the future, for decision making. This endeavor aims to build a deep learning based model for the prediction of breast cancer with a better accuracy. A novel deep extreme gradient descent optimization (DEGDO) has been developed for the breast cancer detection. The proposed model consists of two stages of training and validation. The training phase, in turn, consists of three major layers data acquisition layer, preprocessing layer, and application layer. The data acquisition layer takes the data and passes it to preprocessing layer. In the preprocessing layer, noise and missing values are converted to the normalized which is then fed to the application layer. In application layer, the model is trained with a deep extreme gradient descent optimization technique. The trained model is stored on the server. In the validation phase, it is imported to process the actual data to diagnose. This study has used Wisconsin Breast Cancer Diagnostic dataset to train and test the model. The results obtained by the proposed model outperform many other approaches by attaining 98.73 % accuracy, 99.60% specificity, 99.43% sensitivity, and 99.48% precision.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.