Effective Breast Cancer Prediction Based on Feature Extraction, Fusion And Selection using Hybrid Methodologies

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 5

Year of Publication : 2023

Authors : G. Rajasekaran, C. Sunitha Ram

10.14445/23488379/IJEEE-V10I5P112

How to Cite?

G. Rajasekaran, C. Sunitha Ram, "Effective Breast Cancer Prediction Based on Feature Extraction, Fusion And Selection using Hybrid Methodologies," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 5, pp. 131-142, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I5P112

Abstract:

Women have been affected by many diseases for decades due to their low immune systems. Especially breast cancer is the second largest and most harmful disease for a woman that, leads to death. The earlier prediction of breast cancer can help for easy treatment and Save lives. Deep Learning methods are implemented in medical fields to attain an effective prediction and higher prediction accuracy. Therefore, in this work, the novel hybrid prediction topology is implemented two feature extractions, feature fusion, feature selection and classification with several algorithms such as Linear Discriminant Analysis (LDA), Canonical Correlation Analysis (CCA), Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM), Transit Search Optimization (TSO), Support Vector Machine (SVM) respectively. The LDA and CNN-LSTM method is used to execute a two-feature extraction, and the CCA model is used to merge the two extracted features, a feature fusion task. Next, the TSO-based feature selection is implemented to minimize the redundant features. At last, the classification is carried out using an SVM method to acquire an efficient breast cancer prediction. The proposed hybrid method has achieved more accurate decision-making and efficient training than the conventional methods. The experimental result of the novel hybrid method acquired a superior performance in terms of precision, Recall, Accuracy, F1 Score, RMSE and MAE as 98.81%, 99.02%, 98.18%, 98.85%, 1.001 and 1.016, respectively.

Keywords:

Breast cancer prediction, Feature extraction, ML/DL methods, CCA based feature fusion, TSO-based selection, Classification, Performance accuracy.

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