Feature Raking and Stacked Sparse Autoencoder based Framework for the Prediction of Breast Cancer

Feature Raking and Stacked Sparse Autoencoder based Framework for the Prediction of Breast Cancer

© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : Atul Kumar Ramotra, Vibhakar Mansotra
DOI :  10.14445/22315381/IJETT-V70I5P213

How to Cite?

Atul Kumar Ramotra, Vibhakar Mansotra, "Feature Raking and Stacked Sparse Autoencoder based Framework for the Prediction of Breast Cancer," International Journal of Engineering Trends and Technology, vol. 70, no. 5, pp. 103-110, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P213

Achieving higher classification accuracy using machine learning is a challenging process. It is important to understand each input variable`s significance and contribution to the target class to accomplish this goal. Learning from the suitable representation of the original feature set also enhances the performance of the learning algorithms. This work proposes a framework based on the feature ranking and feature learning techniques for the prediction of Breast Cancer. The main components of the proposed framework include ranking the input variables using the Pearson Correlation method and feature representation of the dataset using Stacked Sparse Autoencoder. The experimental result shows that the proposed framework has achieved an accuracy of 98.42%.

Disease Prediction, Breast Cancer, Feature Ranking, Pearson Correlation, Stacked Sparse Autoencoder.

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