{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T02:44:44Z","timestamp":1775097884319,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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\u2013max 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\u2013testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics\u2014accuracy, precision, recall, F1-score, specificity, and AUC\u2014alongside 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.<\/jats:p>","DOI":"10.3390\/diagnostics16030377","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T17:52:38Z","timestamp":1769190758000},"page":"377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8533-5410","authenticated-orcid":false,"given":"Hasnain","family":"Iftikhar","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Peshawar, Peshawar 25120, Pakistan"},{"name":"Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7676-1020","authenticated-orcid":false,"given":"Atef F.","family":"Hashem","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4993-9433","authenticated-orcid":false,"given":"Moiz","family":"Qureshi","sequence":"additional","affiliation":[{"name":"Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan"},{"name":"Department of Statistics, University of Sindh, Jamshoro 76080, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1248-9910","authenticated-orcid":false,"given":"Paulo Canas","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Department of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil"}]},{"given":"S. O.","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}]},{"given":"Ronny Ivan","family":"Gonzales Medina","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias e Ingenier\u00edas F\u00edsicas y Formales, Universidad Cat\u00f3lica de Santa Mar\u00eda, Arequipa 04013, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0847-0552","authenticated-orcid":false,"given":"Javier Linkolk","family":"L\u00f3pez-Gonzales","sequence":"additional","affiliation":[{"name":"Escuela de Posgrado, Universidad Peruana Uni\u00f3n, Lima 15468, Peru"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"012071","DOI":"10.1088\/1757-899X\/1022\/1\/012071","article-title":"A review paper on breast cancer detection using deep learning","volume":"1022","author":"Priyanka","year":"2021","journal-title":"IOP Conf. 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