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Framework for a Computer-Aided Treatment Prediction (CATP) System for Breast Cancer

Emad Abd Al Rahman1, Nur Intan Raihana Ruhaiyem1,*, Majed Bouchahma2, Kamarul Imran Musa3

1 School of Computer Sciences, Universiti Sains Malaysia, Penang, 11800, Malaysia
2 School of Computer Science, Higher Colleges of Technology, RAK, UAE
3 Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, 16150, Malaysia

* Corresponding Author: Nur Intan Raihana Ruhaiyem. Email: email

Intelligent Automation & Soft Computing 2023, 36(3), 3007-3028.


This study offers a framework for a breast cancer computer-aided treatment prediction (CATP) system. The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagnosis and frequent screening. Mammography has been the most utilized breast imaging technique to date. Radiologists have begun to use computer-aided detection and diagnosis (CAD) systems to improve the accuracy of breast cancer diagnosis by minimizing human errors. Despite the progress of artificial intelligence (AI) in the medical field, this study indicates that systems that can anticipate a treatment plan once a patient has been diagnosed with cancer are few and not widely used. Having such a system will assist clinicians in determining the optimal treatment plan and avoid exposing a patient to unnecessary hazardous treatment that wastes a significant amount of money. To develop the prediction model, data from 336,525 patients from the SEER dataset were split into training (80%), and testing (20%) sets. Decision Trees, Random Forest, XGBoost, and CatBoost are utilized with feature importance to build the treatment prediction model. The best overall Area Under the Curve (AUC) achieved was 0.91 using Random Forest on the SEER dataset.


Cite This Article

E. A. A. Rahman, N. I. R. Ruhaiyem, M. Bouchahma and K. I. Musa, "Framework for a computer-aided treatment prediction (catp) system for breast cancer," Intelligent Automation & Soft Computing, vol. 36, no.3, pp. 3007–3028, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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