The BCPM method: decoding breast cancer with machine learning

Almarri, Badar; Gupta, Gaurav; Kumar, Ravinder; Vandana, Vandana; Asiri, Fatima; Khan, Surbhi Bhatia

Abstract

Breast cancer prediction and diagnosis are critical for timely and effective treatment, significantly impacting patient outcomes. Machine learning algorithms have become powerful tools for improving the prediction and diagnosis of breast cancer. The Breast Cancer Prediction and Diagnosis Model (BCPM), which utilises machine learning techniques to improve the precision and efficiency of breast cancer diagnosis and prediction, is presented in this paper. BCPM collects comprehensive and high-quality data from diverse sources, including electronic medical records, clinical trials, and public datasets. Through rigorous pre-processing, the data is cleaned, inconsistencies are addressed, and missing values are handled. Feature scaling techniques are applied to normalize the data, ensuring fair comparison and equal importance among different features. Furthermore, feature-selection algorithms are utilized to identify the most relevant features that contribute to breast cancer projection and diagnosis, optimizing the model’s efficiency. The BCPM employs numerous machine learning methods, such as logistic regression, random forests, decision trees, support vector machines, and neural networks, to generate accurate models. Area under the curve (AUC), sensitivity, specificity, and accuracy are only some of the metrics used to assess a model’s performance once it has been trained on a subset of data. The BCPM holds promise in improving breast cancer prediction and diagnosis, aiding in personalized treatment planning, and ultimately taming patient results. By leveraging machine learning algorithms, the BCPM contributes to ongoing efforts in combating breast cancer and saving lives.

Citation

Almarri, B., Gupta, G., Kumar, R., Vandana, V., Asiri, F., & Khan, S. B. (in press). The BCPM method: decoding breast cancer with machine learning. BMC Medical Imaging, 24, Article 248. https://doi.org/10.1186/s12880-024-01402-5

• Journal Article Type: Article
• Acceptance Date: Aug 19, 2024
• Online Publication Date: Sep 17, 2024
• Deposit Date: Oct 10, 2024
• Publicly Available Date: Oct 10, 2024
• Journal: BMC Medical Imaging
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 24
• Article Number: 248
• DOI: https://doi.org/10.1186/s12880-024-01402-5
• Keywords: Disease classification, Breast neoplasms, Decision tree, Random forest, Machine learning technique, Transfer of learning
• Additional Information: Correction: https://doi.org/10.1186/s12880-024-01451-w