Development and Evaluation of a Multi-Algorithm Application for Predicting Breast Cancer Patient Survival
DOI:
https://doi.org/10.63158/journalisi.v8i3.1665Keywords:
Machine Learning, Survival Prediction, Decision Support System, Breast Cancer, Multi-class ClassificationAbstract
This study developed a multi-algorithm machine learning prototype for multiclass breast cancer survival prediction using 1,980 patient records, classifying patients as Living, Died of Disease, or Died of Other Causes. The framework integrated NN, SVM, RF, NB, and KNN algorithms within a decision-support monitoring application, with preprocessing steps including data cleaning, normalization, feature preparation, and dataset partitioning. To prevent target leakage, survival-related variables were excluded from the predictor set. The revised evaluation results indicated that NB and KNN delivered the strongest performance, achieving weighted average F1-scores of 0.93 and 0.92, respectively, while NN and RF showed comparatively lower results. These findings highlight the potential of machine learning for breast cancer survival status monitoring, although the proposed system is designed as a decision-support prototype rather than a clinical diagnostic tool. Therefore, before actual healthcare deployment, more research incorporating explainable AI techniques, external validation, and real-world clinical testing is needed.
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[1] J. Potsangbam and S. Shuleenda Devi, “Classification of Breast Cancer Histopathological Images Using Transfer Learning with DenseNet121,” Procedia Comput. Sci., vol. 235, pp. 1990–1997, 2024, doi: 10.1016/j.procs.2024.04.188.
[2] K. Mallikharjuna Rao, G. Saikrishna, and K. Supriya, “Data preprocessing techniques: emergence and selection towards machine learning models - a practical review using HPA dataset,” Multimed. Tools Appl., vol. 82, no. 24, pp. 37177–37196, 2023, doi: 10.1007/s11042-023-15087-5.
[3] A. Chandra, A. S. Kadam, P. Jain, M. Kumawat, H. Y. Patil, and M. A. Gawas, “Breast Cancer Classification using Metaheuristic Optimization and Machine Learning,” 2024 3rd Int. Conf. Artif. Intell. Internet Things, AIIoT 2024, no. AIIoT, pp. 1–4, 2024, doi: 10.1109/AIIoT58432.2024.10574626.
[4] J. Ma, S. Chen, and J. Sun, “Breast Cancer Diagnostic Model Based on RIME-SVM,” 2024 IEEE 6th Int. Conf. Power, Intell. Comput. Syst. ICPICS 2024, pp. 402–406, 2024, doi: 10.1109/ICPICS62053.2024.10796222.
[5] S. Benghazouani, S. Nouh, and A. Zakrani, “Optimizing breast cancer diagnosis: Harnessing the power of nature-inspired metaheuristics for feature selection with soft voting classifiers,” Int. J. Cogn. Comput. Eng., vol. 6, no. February 2024, pp. 1–20, 2025, doi: 10.1016/j.ijcce.2024.09.005.
[6] A. Kaur and S. Gupta, “Unveiling Precision in Breast Cancer Prediction with Random Forest and Decision Trees,” Proc. 5th Int. Conf. Smart Electron. Commun. ICOSEC 2024, no. Icosec, pp. 1232–1236, 2024, doi: 10.1109/ICOSEC61587.2024.10722493.
[7] T. H. Rafi, R. M. Shubair, F. Farhan, M. Z. Hoque, and F. M. Quayyum, “Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms with Optimization Techniques,” IEEE Access, vol. 9, pp. 137847–137868, 2021, doi: 10.1109/ACCESS.2021.3108892.
[8] X. Xiong et al., “Breast cancer: pathogenesis and treatments,” Signal Transduct. Target. Ther., vol. 10, no. 1, 2025, doi: 10.1038/s41392-024-02108-4.
[9] A. Choi et al., “Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department,” Sci. Rep., vol. 13, no. 1, pp. 1–10, 2023, doi: 10.1038/s41598-023-35617-3.
[10] M. Umer, S. Sadiq, H. Karamti, W. Karamti, R. Majeed, and M. Nappi, “IoT Based Smart Monitoring of Patients’ with Acute Heart Failure,” Sensors, vol. 22, no. 7, pp. 1–18, 2022, doi: 10.3390/s22072431.
[11] Z. Zulkifli, F. L. Gaol, T. Agung, and B. Widodo, “Software Testing Integration-Based Model (I-BM) Framework for Recognizing Measure Fault Output Accuracy Using Machine Learning Approach,” Int. J. Softw. Eng. Knowl. Eng., 2023.
[12] L. Balkenende, J. Teuwen, and R. M. Mann, “Application of Deep Learning in Breast Cancer Imaging,” Semin. Nucl. Med., vol. 52, no. 5, pp. 584–596, 2022, doi: 10.1053/j.semnuclmed.2022.02.003.
[13] V. Nemade and V. Fegade, “Machine Learning Techniques for Breast Cancer Prediction,” Procedia Comput. Sci., vol. 218, no. 2022, pp. 1314–1320, 2022, doi: 10.1016/j.procs.2023.01.110.
[14] Y. Supriya and R. Chengoden, “Breast cancer prediction using Shapely and Game theory in Federated Learning environment,” IEEE Access, vol. 12, no. June, pp. 123018–123037, 2024, doi: 10.1109/ACCESS.2024.3424934.
[15] Y. Du, C. McNestry, L. Wei, A. M. Antoniadi, F. M. McAuliffe, and C. Mooney, “Machine learning-based clinical decision support systems for pregnancy care: A systematic review,” Int. J. Med. Inform., vol. 173, no. January, p. 105040, 2023, doi: 10.1016/j.ijmedinf.2023.105040.
[16] V. Nimbalkar and R. Kalra, “A Human-Centered Approach to Interpretable Machine Learning in Clinical Decision Support Systems,” 2025 2nd Int. Conf. Artif. Intell. Innov. Healthc. Ind., pp. 1–5, doi: 10.1109/ICAIIHI67124.2025.11403473.
[17] R. Alharbi, “Breast Cancer Gene Expression Profiles (METABRIC).” [Online]. Available: https://www.kaggle.com/datasets/raghadalharbi/breast-cancer-gene-expression-profiles-metabric
[18] P. Singh and S. Verma, “Multi-classifier model for software fault prediction,” Int. Arab J. Inf. Technol., vol. 15, no. 5, pp. 912–919, 2018.
[19] A. M. Ismael and A. Şengür, “Deep learning approaches for COVID-19 detection based on chest X-ray images,” Expert Syst. Appl., vol. 164, no. March 2020, 2021, doi: 10.1016/j.eswa.2020.114054.
[20] S. Goyal, Handling Class-Imbalance with KNN (Neighbourhood) Under-Sampling for Software Defect Prediction, vol. 55, no. 3. Springer Netherlands, 2022. doi: 10.1007/s10462-021-10044-w.
[21] S. S. Cross, R. F. Harrison, and R. L. Kennedy, Introduction to neural networks, vol. 346, no. 8982. 1995. doi: 10.1016/S0140-6736(95)91746-2.
[22] I. Aydin, M. Karaköse, and E. Akin, “Artificial immune based support vector machine algorithm for fault diagnosis of induction motors,” Int. Aegean Conf. Electr. Mach. Power Electron. Electromotion ACEMP’07 Electromotion’07 Jt. Conf., pp. 217–221, 2007, doi: 10.1109/ACEMP.2007.4510505.
[23] S. Goyal, “Handling Class-Imbalance with KNN (Neighbourhood) Under-Sampling for Software Defect Prediction,” Artif. Intell. Rev., vol. 55, no. 3, pp. 2023–2064, 2022, doi: 10.1007/s10462-021-10044-w.
[24] E. Dritsas and M. Trigka, “Data-Driven Machine-Learning Methods for Diabetes Risk Prediction,” Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145304.
[25] S. A. Ali, N. R. Roy, and D. Raj, “Software Defect Prediction using Machine Learning,” Proc. 17th INDIACom; 2023 10th Int. Conf. Comput. Sustain. Glob. Dev. INDIACom 2023, no. Icoei, pp. 639–642, 2023.
[26] O. Esteban, C. J. Markiewicz, R. W. Blair, C. A. Moodie, and ..., “fMRIPrep: a robust preprocessing pipeline for functional MRI,” Nat Methods, vol. 16, no. 1, pp. 111–116, 2019, doi: https://doi.org/10.1038/s41592-018-0235-4.
[27] T. Byun, V. Sharma, A. Vijayakumar, and ..., “Input prioritization for testing neural networks,” IEEE Int. Conf. Artif. Intell. Test., 2019.
[28] H. Wang, R. Czerminski, and A. C. Jamieson, “Neural Networks and Deep Learning,” Mach. Age Cust. Insight, pp. 91–101, 2021, doi: 10.1108/978-1-83909-694-520211010.
[29] Y. Niu, “Adaptive two-SVM multi-objective cuckoo search algorithm for software defect prediction,” Int. J. Comput. Sci. Math., vol. 9, no. 6, pp. 547–554, 2018, doi: 10.1504/IJCSM.2018.096327.
[30] T. Meeradevi, S. Sasikala, L. Murali, N. Manikandan, and K. Ramaswamy, “Lung cancer detection with machine learning classifiers with multi-attribute decision-making system and deep learning model,” Sci. Rep., vol. 15, no. 1, pp. 1–19, 2025, doi: 10.1038/s41598-025-88188-w.
[31] Y. Xu, C. Peng, M. Li, Y. Li, and S. Du, “Pyramid Feature Attention Network for Monocular Depth Prediction,” Proc. - IEEE Int. Conf. Multimed. Expo, 2021, doi: 10.1109/ICME51207.2021.9428446.
[32] S. Ruuska, W. Hämäläinen, S. Kajava, M. Mughal, P. Matilainen, and J. Mononen, “Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle,” Behav. Processes, vol. 148, pp. 56–62, 2018, doi: 10.1016/j.beproc.2018.01.004.
[33] M. Mustaqeem, “Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection,” Cluster Comput., vol. 24, no. 3, pp. 2581–2595, 2021, doi: 10.1007/s10586-021-03282-8.
[34] Y. Soe, “Software defect prediction using random forest algorithm,” 2018. doi: 10.1109/SEATUC.2018.8788881.
[35] Z. Zulkifli, F. A. Makkiyah, D. Antoni, F. Fitriana, T. Jamaan, and A. Taufik, “Multi-Algorithm to Measure the Accuracy Level of Diabetes Status Prediction,” J. Appl. Data Sci., vol. 5, no. 2, pp. 736–746, 2024, doi: 10.47738/jads.v5i2.250.
[36] K. Kalasampath, K. N. Spoorthi, S. Sajeev, S. S. Kuppa, K. Ajay, and A. Maruthamuthu, “A Literature Review on Applications of Explainable Artificial Intelligence (XAI),” IEEE Access, vol. 13, no. February, pp. 41111–41140, 2025, doi: 10.1109/ACCESS.2025.3546681.
[37] M. Sharma Timilsina, S. Sen, B. Uprety, V. B. Patel, P. Sharma, and P. N. Sheth, “Prediction of HHV of fuel by Machine learning Algorithm: Interpretability analysis using Shapley Additive Explanations (SHAP),” Fuel, vol. 357, no. PA, p. 129573, 2024, doi: 10.1016/j.fuel.2023.129573.
[38] K. Davagdorj, M. Li, and K. H. Ryu, “Local Interpretable Model-Agnostic Explanations of Predictive Models for Hypertension BT - Advances in Intelligent Information Hiding and Multimedia Signal Processing,” J.-S. Pan, J. Li, K. H. Ryu, Z. Meng, and A. Klasnja-Milicevic, Eds., Singapore: Springer Singapore, 2021, pp. 426–433.
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