Published June 7, 2023 | Version CC BY-NC-ND 4.0
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Breast Cancer Prediction Based on Feature Extraction using Hybrid Methodologies

  • 1. Research Scholar, Department of Computer Science and Engineering, SCSVMV University, Kancheepuram (Tamil Nadu), India.
  • 2. Assistant Professor, Department of Computer Science and Engineering, SCSVMV University, Kancheepuram (Tamil Nadu), India.

Contributors

Contact person:

  • 1. Research Scholar, Department of Computer Science and Engineering, SCSVMV University, Kancheepuram (Tamil Nadu), India.

Description

Abstract- The breast cancer prediction is essential for effective treatment and management of the disease. Using data mining techniques to develop predictive models can assist in identifying patients at high risk of developing breast cancer, allowing for early detection and treatment. Early detection has been shown to improve patient outcomes and survival rates. The proposed system for breast cancer prediction involves two main techniques: Linear Discriminant Analysis (LDA) based feature extraction and hyperparameter tuned LSTM-XGBoost based hybrid modelling. The LDA is used to extract the features from the input data that can be trainedusinga hybrid model such as LSTM and XGBoost. The hyperparameters of both models are optimized using cross-validation techniques to achieve high accuracy in breast cancer prediction. Overall, this proposed system has achieved an accuracy and efficiency of breast cancer prediction than existing.

Notes

Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved.

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Subjects

ISSN: 2231-2307 (Online)
https://portal.issn.org/resource/ISSN/2231-2307#
Retrieval Number: 100.1/ijsce.B36120513223
https://www.ijsce.org/portfolio-item/b36120513223/
Journal Website: www.ijsce.org
https://www.ijsce.org/
Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
https://www.blueeyesintelligence.org/