Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques.
Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques.
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DOI：
10.1155/2022/5869529
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发表时间：
2022
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乳腺癌是全球女性死亡人数增加的主要原因之一。乳腺癌细胞的复杂性质（微钙化和肿块）使得放射科医生很难正确诊断。随后，各种计算机辅助诊断（CAD）系统先前已经被开发并且被用于辅助放射科医师诊断癌细胞。然而，由于与延迟和/或不正确的诊断相关的内在风险，改进已开发的诊断系统是必不可少的。在这方面，机器学习最近在乳腺癌的早期和精确检测方面发挥了潜在的作用。本文提出了一种新的基于机器学习的框架，该框架利用随机森林，梯度提升，支持向量机，人工神经网络和多层感知方法，从患者数据中有效地预测乳腺癌。为此，威斯康星州诊断乳腺癌（WDBC）数据集已被利用和分类使用混合多层感知器模型（MLP）和5倍交叉验证框架作为工作原型。对于改进的分类，基于连接的特征选择技术已被使用，也消除了递归功能。所提出的框架已在两个独立的数据集上得到验证，即，威斯康星州预后数据集（WPBC）和威斯康星州原始乳腺癌（WOBC）数据集。结果表明，由于有效的数据预处理和特征选择应用于输入数据，提高了99.12%的准确率。
Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and Multilayer Perception approaches to efficiently predict breast cancer from the patient data. For this purpose, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been utilized and classified using a hybrid Multilayer Perceptron Model (MLP) and 5-fold cross-validation framework as a working prototype. For the improved classification, a connection-based feature selection technique has been used that also eliminates the recursive features. The proposed framework has been validated on two separate datasets, i.e., the Wisconsin Prognostic dataset (WPBC) and Wisconsin Original Breast Cancer (WOBC) datasets. The results demonstrate improved accuracy of 99.12% due to efficient data preprocessing and feature selection applied to the input data.
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DOI：
10.1016/j.compeleceng.2020.106960
发表时间：
2021-03
期刊：
Computers & electrical engineering : an international journal
影响因子：
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作者：
Khan MA;Kadry S;Zhang YD;Akram T;Sharif M;Rehman A;Saba T
通讯作者：
Saba T
超声图像中乳腺癌肿瘤等级的计算机辅助评估。
影响因子：
--
作者：
Chen DR;Chien CL;Kuo YF
通讯作者：
Kuo YF
影响因子：
3.8
作者：
RAVDIN, PM;CLARK, GM
通讯作者：
CLARK, GM
影响因子：
8.5
作者：
Marcano-Cedeno, A.;Quintanilla-Dominguez, J.;Andina, D.
通讯作者：
Andina, D.