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The paper presents the Breast Cancer Prediction and Diagnosis Model (BCPM), a machine-learning framework designed to enhance diagnostic precision and efficiency. It gathers high-quality, multi-source data, performs rigorous preprocessing, applies feature scaling and feature selection, and trains models including logistic regression, random forests, decision trees, support vector machines, and neural networks. 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BMC Medical Imaging (2024) 24:248  \n[https://doi.org/10.1186/s12880-024-01402-5](https://doi.org/10.1186/s12880-024-01402-5)  \nBMC Medical Imaging  \nRESEARCH Open Access  \nThe BCPM method: decoding breast cancer with machine learning  \nBadar Almarri 1*, Gaurav Gupta2, Ravinder Kumar2, Vandana Vandana3, Fatima Asiri4 and Surbhi Bhatia Khan5,6*  \nAbstract  \nBreast 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.  \nKeywords Breast neoplasms, Transfer of learning, Machine learning technique, Random forest, Decision tree, Disease classification  \n*Correspondence: Badar Almarri[baal marri@kfu.edu.sa](baal marri@kfu.edu.sa)[ ](baal marri@kfu.edu.sa)Surbhi Bhatia Khan  \n[s.khan138@salford.ac.uk](s.khan138@salford.ac.uk)  \n1College of Computer Sciences and Information Technology, King Faisal University, Alhasa, Saudi Arabia  \n2Yogananda School of AI, Computers and Data Sciences, Shoolini University, Solan 173212, Himachal Pradesh, India  \n3School of Bioengineering & Food Technology, Shoolini University, Solan 173212, Himachal Pradesh, India  \n4College of Computer Science, Informatics and Computer Systems Department, King Khalid University, Abha, Saudi Arabia  \n5School of Science, Engineering and Environment, University of Salford, Manchester, UK  \n6University Centre for Research and Development, Chandigarh University, Punjab, India  \nIntroduction  \nDespite improvements in diagnosis and therapy, breast cancer is the top cause of death for women globally, presenting challenges for patients, healthcare professionals, and researchers. Customized treatment strategies require accurate prognosis [1]. An AI branch called machine learning trains algorithms on massive datasets to find patterns and predict. It’s increasingly used in medicine for prognosis and diagnosis. These algorithms search large patient datasets for breast cancer prognosis trendsand factors. Analyzing breast cancer cell gene expression with artificial neural networks can predict patient outcomes. Machine learning can predict breast cancer  \n© The Author(s) 2024, corrected publication 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercialNoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit","cbCailvjkBqfohvj","https://ap.wps.com/l/cbCailvjkBqfohvj","pdf",1679892,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What problem does the BCPM method address?\",\"answer\":\"It targets breast cancer prediction and diagnosis to support timely and effective treatment decisions and improve patient outcomes.\"},{\"question\":\"How does the BCPM method prepare and refine its data?\",\"answer\":\"It collects diverse high-quality data, applies rigorous preprocessing to clean it, handles missing values, uses feature scaling to normalize inputs, and applies feature-selection algorithms to keep the most relevant features.\"},{\"question\":\"Which machine learning models and evaluation metrics are used?\",\"answer\":\"The method employs logistic regression, random forests, decision trees, support vector machines, and neural networks. Model performance is assessed with metrics including AUC, sensitivity, specificity, and accuracy.\"}]","The BCPM method: decoding breast cancer with machine learning | PDF",1789981702,25]