Feature Raking and Stacked Sparse Autoencoder based Framework for the Prediction of Breast Cancer
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Atul Kumar Ramotra, Vibhakar Mansotra
|DOI : 10.14445/22315381/IJETT-V70I5P213|
MLA Style: Atul Kumar Ramotra, and 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, May. 2022, pp. 103-110. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P213
APA Style:Atul Kumar Ramotra, & Vibhakar Mansotra. (2022). Feature Raking and Stacked Sparse Autoencoder based Framework for the Prediction of Breast Cancer. International Journal of Engineering Trends and Technology, 70(5), 103-110. 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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