An improved breast cancer disease prediction system using ML and PCA

• Published in: Multimedia tools and applications Vol. 83; no. 11; pp. 33785 - 33821
• Main Authors: Laghmati, Sara, Hamida, Soufiane, Hicham, Khadija, Cherradi, Bouchaib, Tmiri, Amal
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2024; Springer Nature B.V
• Subjects: Accuracy; Breast cancer; Classification; Computer Communication Networks; Computer Science; Correlation coefficients; Data Structures and Information Theory; Datasets; Machine learning; Mammography; Medical research; Multimedia Information Systems; Performance evaluation; Recall; Regression analysis; Regression models; Special Purpose and Application-Based Systems; Supervised learning; Track 2: Medical Applications of Multimedia; CAD systems for breast cancer prediction; Machine Learning; Artificial Intelligence; Feature selection; Stacking with logistic regression; PCA; Majority voting; Flask

Computer-aided diagnosis (CAD) systems based on machine learning (ML) techniques have altered the field of medical research. The deployement of such models to classify breast cancer is one area of many where exactness has been the main preoccupation. CAD systems aim to reach the performance of train...