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Backpropagation Neural Network for Processing of Missing Data in Breast Cancer Detection - 20/07/21

Doi : 10.1016/j.irbm.2021.06.010 
L. Zhang a, b, , H. Cui a, b , B. Liu a, b , C. Zhang c , B.K.P. Horn d
a State Key Lab of Networking & Switching Technology, Beijing University of the Posts and Telecommunications, Beijing, 100876, PR China 
b Beijing Lab of Advanced Information Networks, Beijing, 100876, PR China 
c Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, 10065, USA 
d Computer Science and Artificial Intelligence Laboratory (CSAIL), Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA 

Corresponding author at: Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Haidian District, Beijing, 100876, PR China.Beijing University of Posts and Telecommunications10 Xitucheng RoadHaidian DistrictBeijing100876PR China
Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Tuesday 20 July 2021

Highlights

The applications of Artificial intelligence in biomedical field.
Assistance for missing data processing in biomedical field.
Backpropagation neural network interpolation is high accuracy with rate of 84% and low bias.

Le texte complet de cet article est disponible en PDF.

Abstract

Background

A complete dataset is essential for biomedical implementation. Due to the limitation of objective or subjective factors, missing data often occurs, which exerts uncertainty in the subsequent data processing. Commonly used methods of interpolation are interpolating substitute values that keep minimum error. Some applications of statistics are usually used for handling this problem.

Methods

We are trying to find a higher performance interpolation method compared with the usual statistic methods, by using artificial intelligence which is in full swing today. The prediction and classification of backpropagation neural network are used in this paper, describes a missing data interpolation method to propose the interpolation model that mines association rules in the data. In the experiment, depending on a multi-layer network structure, the model is trained and tested by sample data, constantly revises network weights and thresholds. The error function decreases along the negative gradient direction and approaches the expected real output. The model is validated on the breast cancer dataset, and we select real samples from the data set for validation, moreover, add four traditional methods as a control group.

Results

The proposed method has great performance improvement in the interpolation of missing data. Experimental results show that the interpolation accuracy of our proposed method (84%) is higher than four traditional methods (1.33%, 74.67%, 73.33%, 77.33%) as mentioned in this paper, BPNN stays low in MSE evaluation. Finally, we analyze the performance of various methods in processing missing data.

Conclusions

The study in this paper has estimated missing data with high accuracy as much as possible to reduce the negative impact in the diagnosis of real life. At the same time, it can also assist in missing data processing in the biomedical field.

Le texte complet de cet article est disponible en PDF.

Keywords : Missing data, Interpolation, Backpropagation neural network, Breast cancer, Improvement


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