An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems
Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improvin...
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | Ver en el sistema de origen Ver en el sistema de origen Enlace permanente (DOI o Handle) Enlace permanente (DOI o Handle) |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1881508903844315136 |
|---|---|
| author | Iftikhar, Hasnain Hashem, Atef F. Qureshi, Moiz Rodrigues, Paulo Canas Ali, S. O. Gonzales Medina, Ronny Ivan López Gonzales, Javier Linkolk |
| author_browse | Ali, S. O. Gonzales Medina, Ronny Ivan Hashem, Atef F. Iftikhar, Hasnain López Gonzales, Javier Linkolk Qureshi, Moiz Rodrigues, Paulo Canas |
| author_facet | Iftikhar, Hasnain Hashem, Atef F. Qureshi, Moiz Rodrigues, Paulo Canas Ali, S. O. Gonzales Medina, Ronny Ivan López Gonzales, Javier Linkolk |
| author_sort | Iftikhar, Hasnain |
| collection | Producción científica |
| description | Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival outcomes. However, due to the complexity and heterogeneity of medical data, achieving high predictive accuracy remains a significant challenge. This study proposes an intelligent hybrid system that integrates traditional machine learning (ML), deep learning (DL), and ensemble learning approaches for enhanced breast cancer prediction using the Wisconsin Breast Cancer Dataset. Methods: The proposed system employs a multistage framework comprising three main phases: (1) data preprocessing and balancing, which involves normalization using the min–max technique and application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance; (2) model development, where multiple ML algorithms, DL architectures, and a novel ensemble model are applied to the preprocessed data; and (3) model evaluation and validation, performed under three distinct training–testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics—accuracy, precision, recall, F1-score, specificity, and AUC—alongside graphical analyses and rigorous statistical tests to evaluate predictive consistency. Results: The findings demonstrate that the proposed ensemble model significantly outperforms individual machine learning and deep learning models in terms of predictive accuracy, stability, and reliability. A comparative analysis also reveals that the ensemble system surpasses several state-of-the-art methods reported in the literature. Conclusions: The proposed intelligent hybrid system offers a promising, multidisciplinary approach for improving diagnostic decision support in breast cancer prediction. By integrating advanced data preprocessing, machine learning, and deep learning paradigms within a unified ensemble framework, this study contributes to the broader goals of precision oncology and AI-driven healthcare, aligning with global efforts to enhance early cancer detection and personalized medical care. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/394 |
| institution | UPeU |
| language | English |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/3942026-10-30T16:14:36ZAn Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare SystemsIftikhar, HasnainHashem, Atef F.Qureshi, MoizRodrigues, Paulo CanasAli, S. O.Gonzales Medina, Ronny IvanLópez Gonzales, Javier Linkolkdecision-makingMachine Learning ModelsSustainable Development GoalsBreast Cancer PredictionEnsemble ModelDeep Learning ModelsHybrid Intelligent SystemBackground/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival outcomes. However, due to the complexity and heterogeneity of medical data, achieving high predictive accuracy remains a significant challenge. This study proposes an intelligent hybrid system that integrates traditional machine learning (ML), deep learning (DL), and ensemble learning approaches for enhanced breast cancer prediction using the Wisconsin Breast Cancer Dataset. Methods: The proposed system employs a multistage framework comprising three main phases: (1) data preprocessing and balancing, which involves normalization using the min–max technique and application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance; (2) model development, where multiple ML algorithms, DL architectures, and a novel ensemble model are applied to the preprocessed data; and (3) model evaluation and validation, performed under three distinct training–testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics—accuracy, precision, recall, F1-score, specificity, and AUC—alongside graphical analyses and rigorous statistical tests to evaluate predictive consistency. Results: The findings demonstrate that the proposed ensemble model significantly outperforms individual machine learning and deep learning models in terms of predictive accuracy, stability, and reliability. A comparative analysis also reveals that the ensemble system surpasses several state-of-the-art methods reported in the literature. Conclusions: The proposed intelligent hybrid system offers a promising, multidisciplinary approach for improving diagnostic decision support in breast cancer prediction. By integrating advanced data preprocessing, machine learning, and deep learning paradigms within a unified ensemble framework, this study contributes to the broader goals of precision oncology and AI-driven healthcare, aligning with global efforts to enhance early cancer detection and personalized medical care.Articlehttps://pubmed.ncbi.nlm.nih.gov/41681695https://www.mdpi.com/2075-4418/16/3/377/pdf?version=1769173969https://doi.org/10.3390/diagnostics16030377PMC1289642910.3390/diagnostics16030377https://hdl.handle.net/20.500.15037/39441681695W7125492996enDiagnostics2075-44182026-01-232026-06-13T08:12:36Z2026-06-13T08:12:36Z |
| spellingShingle | decision-making Machine Learning Models Sustainable Development Goals Breast Cancer Prediction Ensemble Model Deep Learning Models Hybrid Intelligent System Iftikhar, Hasnain Hashem, Atef F. Qureshi, Moiz Rodrigues, Paulo Canas Ali, S. O. Gonzales Medina, Ronny Ivan López Gonzales, Javier Linkolk An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems |
| title | An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems |
| title_full | An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems |
| title_fullStr | An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems |
| title_full_unstemmed | An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems |
| title_short | An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems |
| title_sort | intelligent hybrid ensemble model for early detection of breast cancer in multidisciplinary healthcare systems |
| topic | decision-making Machine Learning Models Sustainable Development Goals Breast Cancer Prediction Ensemble Model Deep Learning Models Hybrid Intelligent System |
| topic_facet | decision-making Machine Learning Models Sustainable Development Goals Breast Cancer Prediction Ensemble Model Deep Learning Models Hybrid Intelligent System |
| url | https://pubmed.ncbi.nlm.nih.gov/41681695 https://www.mdpi.com/2075-4418/16/3/377/pdf?version=1769173969 https://doi.org/10.3390/diagnostics16030377 https://hdl.handle.net/20.500.15037/394 |
| work_keys_str_mv | AT iftikharhasnain anintelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems AT hashemateff anintelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems AT qureshimoiz anintelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems AT rodriguespaulocanas anintelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems AT aliso anintelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems AT gonzalesmedinaronnyivan anintelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems AT lopezgonzalesjavierlinkolk anintelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems AT iftikharhasnain intelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems AT hashemateff intelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems AT qureshimoiz intelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems AT rodriguespaulocanas intelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems AT aliso intelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems AT gonzalesmedinaronnyivan intelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems AT lopezgonzalesjavierlinkolk intelligenthybridensemblemodelforearlydetectionofbreastcancerinmultidisciplinaryhealthcaresystems |