基于Lasso-BP神经网络的乳腺肿瘤检测研究

Yanrong Zhang, Lingyue Meng, Yan Liu, Jiayuan Sun
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引用次数: 0

摘要

近年来,乳腺癌作为中国危害女性健康最严重的肿瘤之一,以每年2%的增长速度影响着女性的健康。采用Lasso算法对乳腺癌数据特征进行筛选,然后利用BP神经网络对UCI数据集中的9个乳腺癌数据决定因素和筛选后的其余8个决定因素进行分类。实验结果表明:在基于BP神经网络的乳腺癌检测中,利用剩余的8个乳腺癌数据特征对良恶性肿瘤进行分类,分类准确率高于原始的9个乳腺癌数据特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on Breast Tumor Detection Based on Lasso-BP Neural Network
In recent years, breast cancer, as one of the most threatening tumors for women's health in China, affects women's health with a growth rate of 2% every year. The Lasso algorithm was used to screen the characteristics of breast cancer data, and then the BP neural network was used to classify the 9 breast cancer data determination factors in the UCI dataset and the remaining 8 determination factors after screening. The experimental results showed that: In the detection of breast cancer based on BP neural network, the remaining 8 breast cancer data features are used to classify benign and malignant tumors, and the classification accuracy rate is higher than that of the original 9 breast cancer data features.
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