Classification of breast cancer data based on neural network algorithm

Mingyu Ouyang

doi:10.62051/aht6gj52

Authors

Mingyu Ouyang

DOI:

https://doi.org/10.62051/aht6gj52

Keywords:

BP neural network; correlation analysis; Feature Selection; principal component analysis.

Abstract

In today's world, breast cancer has become the most common malignant tumor, so this paper mainly classifies the features of breast cancer through BP neural network to improve the accuracy of research and judgment. This paper mainly adopts 30 breast cancer feature data of 569 cases, and then obtains the feature data used in this BP neural network through correlation analysis, feature selection and principal component analysis. By training, the BP neural network that is most suitable for this data is constructed from aspects such as the number of hidden layers, loss function, and iteration times, ultimately improving the accuracy to 100%.

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References

Shou Y, Meng T, Ai W, et al. Object Detection in Medical Images Based on Hierarchical Transformer and Mask Mechanism [J]. Computational Intelligence and Neuroscience, 2022, 2022.

Shou Y, Meng T, Ai W, et al. Conversational emotion recognition studies based on graph convolutional neural networks and a dependent syntactic analysis [J]. Neurocomputing, 2022, 501: 629 - 639.

Chen J, Liu Z, Yin Z, et al. Predict the effect of meteorological factors on haze using BP neural network [J]. Urban Climate, 2023, 51: 101630.

Chen L, Jagota V, Kumar A. RETRACTED ARTICLE: Research on optimization of scientific research performance management based on BP neural network [J]. International Journal of System Assurance Engineering and Management, 2023, 14 (1): 489 - 489.

Yang A, Zhuansun Y, Liu C, et al. Design of intrusion detection system for internet of things based on improved BP neural network[J]. Ieee Access, 2019, 7: 106043 - 106052.

Shou Y, Cao X, Meng D, et al. A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition [J]. arXiv preprint arXiv: 2306. 17799, 2023.

Wu Y, Gao R, Yang J. Prediction of coal and gas outburst: A method based on the BP neural network optimized by GASA [J]. Process Safety and Environmental Protection, 2020, 133: 64 - 72.

Shou Y, Cao X, Meng D. Masked Contrastive Graph Representation Learning for Age Estimation [J]. arXiv preprint arXiv: 2306.17798, 2023.

Wen L, Yuan X. Forecasting CO2 emissions in Chinas commercial department, through BP neural network based on random forest and PSO [J]. Science of The Total Environment, 2020, 718: 137194.

Xu L, Wang H, Gulliver T A. Outage probability performance analysis and prediction for mobile IoV networks based on ICS-BP neural network [J]. IEEE Internet of Things Journal, 2020, 8 (5): 3524 - 3533.