BCPM 方法:用机器学习解码乳腺癌

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-09-17 DOI:10.1186/s12880-024-01402-5
Badar Almarri, Gaurav Gupta, Ravinder Kumar, Vandana Vandana, Fatima Asiri, Surbhi Bhatia Khan
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引用次数: 0

摘要

乳腺癌的预测和诊断对于及时有效的治疗至关重要,并对患者的预后产生重大影响。机器学习算法已成为改善乳腺癌预测和诊断的有力工具。本文介绍了乳腺癌预测和诊断模型(BCPM),该模型利用机器学习技术提高了乳腺癌诊断和预测的准确性和效率。BCPM 从电子病历、临床试验和公共数据集等不同来源收集全面、高质量的数据。通过严格的预处理,对数据进行了清理,解决了不一致问题,并处理了缺失值。采用特征缩放技术对数据进行归一化处理,确保不同特征之间的公平比较和同等重要性。此外,还利用特征选择算法来识别有助于乳腺癌预测和诊断的最相关特征,从而优化模型的效率。BCPM 采用了多种机器学习方法,如逻辑回归、随机森林、决策树、支持向量机和神经网络,以生成准确的模型。曲线下面积(AUC)、灵敏度、特异性和准确性只是用于评估模型在子集数据上训练后性能的部分指标。BCPM 有望改善乳腺癌的预测和诊断,帮助制定个性化治疗计划,并最终改善患者的治疗效果。通过利用机器学习算法,BCMM 为抗击乳腺癌和挽救生命的持续努力做出了贡献。
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The BCPM method: decoding breast cancer with machine learning
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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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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