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

Emad Abd Al Rahman¹, Nur Intan Raihana Ruhaiyem^1,*, 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: intanraihana@usm.my

Intelligent Automation & Soft Computing https://doi.org/10.32604/iasc.2023.032580

Received 23 May 2022; Accepted 01 July 2022; Published online 10 January 2023

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Abstract

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.

Keywords

Breast cancer; machine learning; feature importance; feature selection; treatment prediction; SEER dataset; computer-aided treatment prediction (CATP); clinical decision support system