Optimizing Adaptive Boosting Model for Breast Cancer Prediction Using Principal Component Analysis and Random Oversampling Techniques

Donata Yulvida, Ahmad Saikhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages173-178
Number of pages6
ISBN (Electronic)9798350368970
DOIs
Publication statusPublished - 2024
Event8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024 - Hybrid, Yogyakarta, Indonesia
Duration: 29 Aug 202430 Aug 2024

Publication series

Name2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

Conference

Conference8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
Country/TerritoryIndonesia
CityHybrid, Yogyakarta
Period29/08/2430/08/24

Keywords

  • Grid SearchCV
  • Principal Component Analysis
  • Random Oversampling
  • breast cancer
  • machine learning

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