TY - GEN
T1 - Optimizing Adaptive Boosting Model for Breast Cancer Prediction Using Principal Component Analysis and Random Oversampling Techniques
AU - Yulvida, Donata
AU - Saikhu, Ahmad
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Breast cancer is the most common type of cancer in women and remains the leading cause of cancer death among them. Risk factors such as obesity, lack of physical activity, alcohol consumption, hormone therapy during menopause, radiation exposure, and family history plays important roles in its development. Early detection is critical, but machine learning applications for prediction face challenges, particularly due to class imbalance in the dataset, which can seriously impact model performance. This study focuses on optimizing AdaBoost parameters using a combination of PCA and Random Oversampling. The results show that the optimized model achieves 98.24% accuracy in breast cancer prediction. The combination of PCA for feature reduction and Random Oversampling for data balancing effectively improves prediction accuracy. These findings provide a solid foundation for developing more precise diagnostic methods using machine learning in future breast cancer research.
AB - Breast cancer is the most common type of cancer in women and remains the leading cause of cancer death among them. Risk factors such as obesity, lack of physical activity, alcohol consumption, hormone therapy during menopause, radiation exposure, and family history plays important roles in its development. Early detection is critical, but machine learning applications for prediction face challenges, particularly due to class imbalance in the dataset, which can seriously impact model performance. This study focuses on optimizing AdaBoost parameters using a combination of PCA and Random Oversampling. The results show that the optimized model achieves 98.24% accuracy in breast cancer prediction. The combination of PCA for feature reduction and Random Oversampling for data balancing effectively improves prediction accuracy. These findings provide a solid foundation for developing more precise diagnostic methods using machine learning in future breast cancer research.
KW - Grid SearchCV
KW - Principal Component Analysis
KW - Random Oversampling
KW - breast cancer
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85210489291&partnerID=8YFLogxK
U2 - 10.1109/ICITISEE63424.2024.10730573
DO - 10.1109/ICITISEE63424.2024.10730573
M3 - Conference contribution
AN - SCOPUS:85210489291
T3 - 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
SP - 173
EP - 178
BT - 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
Y2 - 29 August 2024 through 30 August 2024
ER -