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The BCPM method: decoding breast cancer with machine learning.
Almarri, Badar; Gupta, Gaurav; Kumar, Ravinder; Vandana, Vandana; Asiri, Fatima; Khan, Surbhi Bhatia.
Afiliación
  • Almarri B; College of Computer Sciences and Information Technology, King Faisal University, Alhasa, Saudi Arabia.
  • Gupta G; Yogananda School of AI, Computers and Data Sciences, Shoolini University, Solan, 173212, Himachal Pradesh, India.
  • Kumar R; Yogananda School of AI, Computers and Data Sciences, Shoolini University, Solan, 173212, Himachal Pradesh, India.
  • Vandana V; School of Bioengineering & Food Technology, Shoolini University, Solan, 173212, Himachal Pradesh, India.
  • Asiri F; College of Computer Science, Informatics and Computer Systems Department, King Khalid University, Abha, Saudi Arabia.
  • Khan SB; School of Science, Engineering and Environment, University of Salford, Manchester, UK. s.khan138@salford.ac.uk.
BMC Med Imaging ; 24(1): 248, 2024 Sep 17.
Article en En | MEDLINE | ID: mdl-39289621
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Aprendizaje Automático Límite: Female / Humans Idioma: En Revista: BMC Med Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Aprendizaje Automático Límite: Female / Humans Idioma: En Revista: BMC Med Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Reino Unido