Intelligent breast cancer diagnostic system empowered by deep extreme gradient descent optimization

• Published in: Mathematical biosciences and engineering : MBE Vol. 19; no. 8; pp. 7978 - 8002
• Main Authors: Khan, Muhammad Bilal Shoaib, Atta-ur-Rahman, Nawaz, Muhammad Saqib, Ahmed, Rashad, Khan, Muhammad Adnan, Mosavi, Amir
• Format: Journal Article
• Language: English
• Published: AIMS Press 01.01.2022
• Subjects: artificial intelligence; breast cancer; decision tree; deep extreme; gradient descent optimization; machine learning; random forest
• ISSN: 1551-0018; 1551-0018
• DOI: 10.3934/mbe.2022373

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.