RT journal article
T1 An Improved Model for Breast Cancer Diagnosis by Combining PCA and Logistic Regression Techniques
A1 Houfani, Djihane
A1 Slatnia, Sihem
A1 Kazar, Okba
A1 Remadna, Ikram
A1 Saouli, Hamza
A1 Ortiz Bellot, Guadalupe
A1 Merizig, Abdelhak
A2 Ingeniería Informática
K1 Breast Cancer
K1 Computer-Aided Diagnosis
K1 Feature Selection
K1 Logistic Regression
K1 Machine Learning
K1 Principal Component Analysis (PCA)
AB Abstract: Breast cancer is weighed one of the most life-threatening illnesses confronting women. It happens when the multiplicationof cells in breast tissue is uncontrollable. Several studies have been performed in the healthcare field for early breast cancer diagnosis.However, traditional methods can generate incomplete or misleading outcomes. To overcome these limitations, computer-aided diagnosis(CAD) systems are extensively exploited in the healthcare domain. It is designed to improve accuracy, decrease complexity, andreduce misclassification costs. The goal of this study is to present a breast cancer CAD system based on combining the PrincipalComponent Analysis (PCA) method for feature reduction and Logistic Regression (LR) for BC tumors classification. The experimentshave been conducted on Wisconsin Diagnosis Breast Cancer (WDBC) and Wisconsin Original Breast Cancer (WOBC) datasets fromUCI repository using different training and testing subsets. Moreover, we carried out extensive comparisons of our approach with otherexisting approaches. Multiple metrics like precision, F1 score, recall, accuracy, and Area Under Curve (AUC) were used in this study.Experimental results indicate that the proposed approach records a remarkable performance rate with an accuracy of 1.00 and 0.98 forWDBC and WOBC respectively and outperforms the previous works by decreasing the number of features, improving the data quality,and reducing the response time.
PB University of Bahrain
SN 2210-142X
YR 2023
FD 2023-02-28
LK http://hdl.handle.net/10498/29738
UL http://hdl.handle.net/10498/29738
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 12-nov-2026