Multi-modal Stacked Ensemble Model for Breast Cancer Prognosis Prediction
Keywords:
Breast cancer prognosis prediction, optimized CNN, Tug of War algorithm, stacked-ensemble learning
Abstract
Breast cancer (BC) is a global health challenge that affects millions of women worldwide and leads to significant mortality. Recent advancements in next-generation sequencing technology have enabled comprehensive diagnosis and prognosis determination using multiple data modalities. Deep learning methods have shown promise in utilizing these multimodal data sources, outperforming single-modal models. However, integrating these heterogeneous data sources poses significant challenges in clinical decision-making. This study proposes an optimized multimodal CNN for a stacked ensemble model (OMCNNSE) for breast cancer prognosis. Our novel method involves the integration of the Tug of War (TWO) algorithm to optimize the hyperparameters of a convolutional neural network (CNN), enhancing feature extraction from three distinct multimodal datasets: clinical profile data, copy number alteration (CNA), and gene expression data. Specifically, we employ the TWO algorithm to optimize separate CNN models for each dataset, identifying optimal values for the hyperparameters. We then trained the three baseline CNN models using the optimized values through 10-fold crossvalidation. Finally, we utilize an ensemble learning approach to integrate the models’ predictions and apply an SVM classifier for the final prediction. To evaluate the proposed method, we conducted experiments on the METABRIC breast cancer dataset comprising diverse patient profiles. Our results demonstrated the effectiveness of the OMCNNSE approach for predicting breast cancer prognosis. The model achieved high AUC, accuracy, sensitivity, precision, and MCC, outperforming traditional single-modal models and other state-of-the-art methods.References
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5. O. Gevaert, F. De Smet, D. Timmerman, Y. Moreau, and B. De Moor, “Predicting the prognosis of breast cancer by integrating clinical
and microarray data with Bayesian networks,” in Bioinformatics, Jul. 2006. doi: 10.1093/bioinformatics/btl230.
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markers,” Bioinformatics, vol. 23, no. 1, pp. 30–37, 2007, doi: 10.1093/bioinformatics/btl543.
7. X. Xu, Y. Zhang, L. Zou, M. Wang, and A. Li, “A gene signature for breast cancer prognosis using support vector machine,”
in 2012 5th International Conference on Biomedical Engineering and Informatics, BMEI 2012, 2012, pp. 928–931. doi:
10.1109/BMEI.2012.6513032.
8. Z. He, J. Zhang, X. Yuan, and Y. Zhang, “Integrating Somatic Mutations for Breast Cancer Survival Prediction Using Machine Learning
Methods,” Front. Genet., vol. 11, no. January, pp. 1–12, 2021, doi: 10.3389/fgene.2020.632901.
9. V. De Vijver, “Numb Er 25 a Gene-Expression Signature As a Predictor of Survival in Breast Cancer,” vol. 347, no. 25, pp. 1999–2009,
2002.
10. Y. Wang et al., “Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer,” Lancet, vol.
365, no. 9460, pp. 671–679, 2005, doi: 10.1016/S0140-6736(05)70933-8.
11. Y. Q. Liu, C. Wang, and L. Zhang, “Decision tree based predictive models for breast cancer survivability on imbalanced data,” 3rd Int.
Conf. Bioinforma. Biomed. Eng. iCBBE 2009, pp. 1–4, 2009, doi: 10.1109/ICBBE.2009.5162571.
12. A. C. Tan and D. Gilbert, “Ensemble machine learning on gene expression data for cancer classification.,” Appl. Bioinformatics, vol.
2, no. 3 Suppl, pp. 1–10, 2003.
13. L. Tong, J. Mitchel, K. Chatlin, and M. D. Wang, “Deep learning based feature-level integration of multi-omics data for breast cancer
patients survival analysis,” BMC Med. Inform. Decis. Mak., vol. 20, no. 1, Sep. 2020, doi: 10.1186/s12911-020-01225-8.
14. A. Dhillon and A. Singh, “EBreCaP: Extreme learning-based model for breast cancer survival prediction,” IET Syst. Biol., vol. 14, no.
3, pp. 160–169, Jun. 2020, doi: 10.1049/iet-syb.2019.0087.
15. J. Gao, T. Lyu, F. Xiong, J. Wang, W. Ke, and Z. Li, “MGNN: A Multimodal Graph Neural Network for Predicting the Survival of
Cancer Patients,” in SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in
Information Retrieval, Association for Computing Machinery, Inc, Jul. 2020, pp. 1697–1700. doi: 10.1145/3397271.3401214.
16. S. Kayikci and T. M. Khoshgoftaar, “Breast cancer prediction using gated attentive multimodal deep learning,” J. Big Data, vol. 10,
no. 1, 2023, doi: 10.1186/s40537-023-00749-w.
17. E. Mustafa, E. K. Jadoon, S. Khaliq-uz-Zaman, M. A. Humayun, and M. Maray, “An Ensembled Framework for Human Breast Cancer
Survivability Prediction Using Deep Learning,” Diagnostics, vol. 13, no. 10, pp. 1–13, 2023, doi: 10.3390/diagnostics13101688.
18. N. A. Othman, M. A. Abdel-Fattah, and A. T. Ali, “A Hybrid Deep Learning Framework with Decision-Level Fusion for Breast Cancer
Survival Prediction,” Big Data Cogn. Comput., vol. 7, no. 1, p. 50, 2023, doi: 10.3390/bdcc7010050.
19. T. Baltruˇ, “Multimodal Machine Learning: A Survey and Taxonomy,” IEEE Trans. Pattern Anal. Mach. Intell., vol. Volume: 41, no.
Issue: 2, pp. 1–20, 2019.
20. L. Hedjazi, M. V. Le Lann, T. Kempowsky-Hamon, F. Dalenc, and G. Favre, “Improved breast cancer prognosis based on a hybrid
marker selection approach,” in BIOINFORMATICS 2011 - Proceedings of the International Conference on Bioinformatics Models,
Methods and Algorithms, 2011, pp. 159–164. doi: 10.5220/0003152301590164.
Stat., Optim. Inf. Comput.
21. M. Khademi and N. S. Nedialkov, “Probabilistic graphical models and deep belief networks for prognosis of breast cancer,” in
Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, Institute of Electrical
and Electronics Engineers Inc., Mar. 2016, pp. 727–732. doi: 10.1109/ICMLA.2015.196.
22. C. Author, “Prognosis Cancer Prediction Model,” vol. 95, no. 20, pp. 5369–5378, 2017.
23. J. Pittman et al., “Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes,”
2004. [Online]. Available: www.pnas.orgcgidoi10.1073pnas.0401736101
24. A. H. Chen and C. Yang, “The improvement of breast cancer prognosis accuracy from integrated gene expression and clinical data,”
Expert Syst. Appl., vol. 39, no. 5, pp. 4785–4795, 2012, doi: 10.1016/j.eswa.2011.09.144.
25. M. Zhao, Y. Tang, H. Kim, and K. Hasegawa, “Machine Learning With K-Means Dimensional Reduction for Predicting Survival
Outcomes in Patients With Breast Cancer,” Cancer Inform., vol. 17, Nov. 2018, doi: 10.1177/1176935118810215.
26. D. Sun, M. Wang, and A. Li, “A Multimodal Deep Neural Network for Human Breast Cancer Prognosis Prediction by
Integrating Multi-Dimensional Data,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 16, no. 3, pp. 841–850, May 2019, doi:
10.1109/TCBB.2018.2806438.
27. N. Arya and S. Saha, “Multi-Modal Classification for Human Breast Cancer Prognosis Prediction: Proposal of Deep-Learning
Based Stacked Ensemble Model,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 19, no. 2, pp. 1032–1041, 2020, doi:
10.1109/TCBB.2020.3018467.
28. N. Arya and S. Saha, “Multi-modal advanced deep learning architectures for breast cancer survival prediction[Formula presented],”
Knowledge-Based Syst., vol. 221, Jun. 2021, doi: 10.1016/j.knosys.2021.106965.
29. Y. Wang, H. Zhang, and G. Zhang, “cPSO-CNN: An efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional
neural networks,” Swarm Evol. Comput., vol. 49, no. December 2018, pp. 114–123, 2019, doi: 10.1016/j.swevo.2019.06.002.
30. Z. Gao, Y. Li, Y. Yang, X. Wang, N. Dong, and H. D. Chiang, “A GPSO-optimized convolutional neural networks for EEG-based
emotion recognition,” Neurocomputing, vol. 380, pp. 225–235, 2020, doi: 10.1016/j.neucom.2019.10.096.
31. N. Bacanin, T. Bezdan, and I. Strumberger, “Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm
Intelligence,” 2020, doi: 10.3390/a13030067.
32. I˙. L. A. L. I˙. O¨ zkan, “Turkish Journal of Electrical Engineering and Computer Sciences The analysis and optimization of CNN
Hyperparameters with fuzzy tree modelfor image classification,” vol. 30, no. 3, 2022, doi: 10.55730/1300-0632.3821.
33. R. O. Ogundokun, S. Misra, M. Douglas, and R. Damaˇseviˇ, “Medical Internet-of-Things Based Breast Cancer Diagnosis Using
Hyperparameter-Optimized Neural Networks,” 2022.
34. M. A. Amou, K. Xia, S. Kamhi, and M. Mouhafid, “A Novel MRI Diagnosis Method for Brain Tumor Classification Based on CNN
and Bayesian Optimization,” pp. 1–21, 2022.
35. H. Respiration, “Hyperparameter Optimization Method Based on Harmony Search Algorithm to Improve Performance Recognition
System,” pp. 1–19, 2020.
36. G. Atteia and N. A. Samee, “CNN-Hyperparameter Optimization for Diabetic Maculopathy Diagnosis in Optical Coherence
Tomography and Fundus Retinography,” pp. 1–30, 2022.
37. E. Kıymac¸ and Y. Kaya, “A novel automated CNN arrhythmia classifier with memory-enhanced artificial hummingbird algorithm,”
Expert Syst. Appl., vol. 213, no. PC, p. 119162, 2023, doi: 10.1016/j.eswa.2022.119162.
38. E. H. Houssein, “An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm,”
vol. 5, pp. 18015–18033, 2022.
39. P. Kaur, A. Singh, and I. Chana, “BSense: A parallel Bayesian hyperparameter optimized Stacked ensemble model for breast cancer
survival prediction,” J. Comput. Sci., vol. 60, no. January, p. 101570, 2022, doi: 10.1016/j.jocs.2022.101570.
40. T. F. Gonzalez, “Handbook of approximation algorithms and metaheuristics,” Handb. Approx. Algorithms Metaheuristics, pp. 1–1432,
2007, doi: 10.1201/9781420010749.
41. K. Balasubramanian, N. P. Ananthamoorthy, and K. Ramya, “An approach to classify white blood cells using convolutional neural
network optimized by particle swarm optimization algorithm,” Neural Comput. Appl., vol. 34, no. 18, pp. 16089–16101, 2022.
42. A. Kaveh and A. Zolghadr, “A NOVEL META-HEURISTIC ALGORITHM: TUG OF WAR,” vol. 6, no. 4, pp. 469–492, 2016.
43. M. Kim and H. Yu, “19th IEEE International Conference on Tools with Artificial Intelligence A New Feature Transformation Method
based on Rotation for Speaker Identification,” pp. 68–73, 2007, doi: 10.1109/ICTAI.2007.49.
44. C. Curtis et al., “The genomic and transcriptomic architecture of 2 , 000 breast tumours,” 2012, doi: 10.1038/nature10983.
45. O. Troyanskaya et al., “Missing value estimation methods for DNA microarrays,” vol. 17, no. 6, pp. 520–525, 2001.
46. Y. Cai, T. Huang, and L. Hu, “Prediction of lysine ubiquitination with mRMR feature selection and analysis,” pp. 1387–1395, 2012,
doi: 10.1007/s00726-011-0835-0.
47. X. Xu, Y. Zhang, L. Zou, M. Wang, and A. Li, “A gene signature for breast cancer prognosis using support vector machine,” 2012 5th
Int. Conf. Biomed. Eng. Informatics, BMEI 2012, no. Bmei, pp. 928–931, 2012, doi: 10.1109/BMEI.2012.6513032
48. C. Nguyen, Y. Wang, and H. N. Nguyen, “Random forest classifier combined with feature selection for breast cancer diagnosis and
prognostic,” J. Biomed. Sci. Eng., vol. 06, no. 05, pp. 551–560, 2013, doi: 10.4236/jbise.2013.65070.
49. M. F. Jefferson, N. Pendleton, S. B. Lucas, and M. A. Horan, “Comparison of genetic algorithm neural network with logistic regression
for predicting outcome after surgery for patients with nonsmall cell lung carcinoma,” Cancer, vol. 79, no. 7, pp. 1338–1342, 1997, doi:
10.1002/(SICI)1097-0142(19970401)79:7¡1338::AID-CNCR10¿3.0.CO;2-0.
50. A. Tharwat, “Classification assessment methods,” Appl. Comput. Informatics, vol. 17, no. 1, pp. 168–192, 2018, doi:
10.1016/j.aci.2018.08.003.
51. K. Tomczak, P. Czerwi´nska, and M. Wiznerowicz, “The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge,”
Wspolczesna Onkol., vol. 1A, pp. A68–A77, 2015, doi: 10.5114/wo.2014.47136.
Published
2024-10-15
How to Cite
Maigari, A., Zainol, Z., & CHEW, X. (2024). Multi-modal Stacked Ensemble Model for Breast Cancer Prognosis Prediction. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2100
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