Breast Cancer Prediction Based on Feature Extraction using Hybrid Methodologies

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Rajasekaran
Dr. C. Sunitha Ram,

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.

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How to Cite
[1]
Rajasekaran and Dr. C. Sunitha Ram, “Breast Cancer Prediction Based on Feature Extraction using Hybrid Methodologies”, IJSCE, vol. 13, no. 2, pp. 20–28, Jul. 2023, doi: 10.35940/ijsce.B36120513223.
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Articles

How to Cite

[1]
Rajasekaran and Dr. C. Sunitha Ram, “Breast Cancer Prediction Based on Feature Extraction using Hybrid Methodologies”, IJSCE, vol. 13, no. 2, pp. 20–28, Jul. 2023, doi: 10.35940/ijsce.B36120513223.

References

Jemal A, Murray T, Ward E, Samuels A, Tiwari RC, Ghafoor A, Feuer EJ, Thun MJ. Cancer statistics, 2005. CA: a cancer journal for clinicians. 2005 Jan 1;55(1):10-30.

M. L. Santilli et al., "Self-Supervised Learning For Detection Of Breast Cancer In Surgical Margins With Limited Data," 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France, 2021, pp. 980-984, doi: 10.1109/ISBI48211.2021.9433829.

Fu, P. Liu, J. Lin, L. Deng, K. Hu and H. Zheng, "Predicting Invasive Disease-Free Survival for Early Stage Breast Cancer Patients Using Follow-Up Clinical Data," in IEEE Transactions on Biomedical Engineering, vol. 66, no. 7, pp. 2053-2064, July 2019, doi: 10.1109/TBME.2018.2882867.

H. Kutrani and S. Eltalhi, "Decision Tree Algorithms for Predictive Modeling in Breast Cancer Treatment," 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), Sabratha, Libya, 2022, pp. 223-227, doi: 10.1109/MI-STA54861.2022.9837762.

Yifan, L. Jialin and F. Boxi, "Forecast Model of Breast Cancer Diagnosis Based on RF-AdaBoost," 2021 International Conference on Communications, Information System and Computer Engineering (CISCE), Beijing, China, 2021, pp. 716-719, doi: 10.1109/CISCE52179.2021.9445847.

M. Sabha and B. Tugrul, "Breast Cancer Prediction Using Different Classification Algorithms with Various Feature Selection Strategies," 2021 5th International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia, 2021, pp. 18-23, doi: 10.1109/ICICoS53627.2021.9651867.

O. Jessica, M. Hamada, S. I. Yusuf and M. Hassan, "The Role of Linear Discriminant Analysis for Accurate Prediction of Breast Cancer," 2021 IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), Singapore, Singapore, 2021, pp. 340-344, doi: 10.1109/MCSoC51149.2021.00057.

R. R. Rath, S. K. Swain, K. Pooranapriya, J. K, M. Deivakani and V. Avasthi, "Breast Cancer Prediction using Deep Network Model with Multi-Modal Data Fusion," 2022 International Conference on Edge Computing and Applications (ICECAA), Tamilnadu, India, 2022, pp. 1192-1197, doi: 10.1109/ICECAA55415.2022.9936361.

S. Pravesjit, P. Longpradit, K. Kantawong, R. Pengchata and N. Oul, "A Hybrid PSO with Rao Algorithm for Classification of Wisconsin Breast Cancer Dataset," 2021 2nd International Conference on Big Data Analytics and Practices (IBDAP), Bangkok, Thailand, 2021, pp. 68-71, doi: 10.1109/IBDAP52511.2021.9552152.

S. Kayikci and T. Khoshgoftaar, "A Stack Based Multimodal Machine Learning Model for Breast Cancer Diagnosis," 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey, 2022, pp. 1-5, doi: 10.1109/HORA55278.2022.9800004.

P. Liu, B. Fu, S. X. Yang, L. Deng, X. Zhong and H. Zheng, "Optimizing Survival Analysis of XGBoost for Ties to Predict Disease Progression of Breast Cancer," in IEEE Transactions on Biomedical Engineering, vol. 68, no. 1, pp. 148-160, Jan. 2021, doi: 10.1109/TBME.2020.2993278.

R. Lupat, R. Perera, S. Loi and J. Li, "Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes," in IEEE Access, vol. 11, pp. 10912-10924, 2023, doi: 10.1109/ACCESS.2023.3240515.

Nathiya S. S and S. G, "SVM-ANN Optimized Algorithm for the Classification of Breast Cancer Data as Benign and Malignant," 2022 Smart Technologies, Communication and Robotics (STCR), Sathyamangalam, India, 2022, pp. 1-7, doi: 10.1109/STCR55312.2022.10009301.

C. -H. Yang, S. -H. Moi, M. -F. Hou, L. -Y. Chuang and Y. -D. Lin, "Applications of Deep Learning and Fuzzy Systems to Detect Cancer Mortality in Next-Generation Genomic Data," in IEEE Transactions on Fuzzy Systems, vol. 29, no. 12, pp. 3833-3844, Dec. 2021, doi: 10.1109/TFUZZ.2020.3028909.

N. Arya and S. Saha, "Multi-Modal Classification for Human Breast Cancer Prognosis Prediction: Proposal of Deep-Learning Based Stacked Ensemble Model," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 19, no. 2, pp. 1032-1041, 1 March-April 2022, doi: 10.1109/TCBB.2020.3018467.

M. Yang, Y. Han, C. -S. Liu, J. -H. Wu and D. -B. Hua, "D-TSVR Recurrence Prediction Driven by Medical Big Data in Cancer," in IEEE Transactions on Industrial Informatics, vol. 17, no. 5, pp. 3508-3517, May 2021, doi: 10.1109/TII.2020.3011675.

Sun, M. Wang and A. Li, "A Multimodal Deep Neural Network for Human Breast Cancer Prognosis Prediction by Integrating Multi-Dimensional Data," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 16, no. 3, pp. 841-850, 1 May-June 2019, doi: 10.1109/TCBB.2018.2806438.

R. Mendonca-Neto, Z. Li, D. Fenyö, C. T. Silva, F. G. Nakamura and E. F. Nakamura, "A Gene Selection Method Based on Outliers for Breast Cancer Subtype Classification," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 19, no. 5, pp. 2547-2559, 1 Sept.-Oct. 2022, doi: 10.1109/TCBB.2021.3132339.

M. Pouryahya et al., "aWCluster: A Novel Integrative Network-Based Clustering of Multiomics for Subtype Analysis of Cancer Data," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 19, no. 3, pp. 1472-1483, 1 May-June 2022, doi: 10.1109/TCBB.2020.3039511.

Li et al., "BCRAM: A Social-Network-Inspired Breast Cancer Risk Assessment Model," in IEEE Transactions on Industrial Informatics, vol. 15, no. 1, pp. 366-376, Jan. 2019, doi: 10.1109/TII.2018.2825345.

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