Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Deepa, B.G.; Velmurugan, R.; Narender, M.; Suhaas, K.P. Title A name given to the resource A data-driven approach to predicting breast cancer recurrence with hybrid machine learning models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Advances in Computational Design;Volume;11;Issue;1;pp.79-96 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.12989/acd.2026.11.1.079" target="_blank" rel="noreferrer noopener">https://doi.org/10.12989/acd.2026.11.1.079</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031064136?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031064136?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Deepa B.G., Department of Computer Science, Christ University, Bangalore, India; Velmurugan R., Department of Computer Science, Kristu Jayanti (Deemed to be University), Bangalore, India; Narender M., Department of Computer Science and Engineering, The National Institute of Engineering, Mysore, India; Suhaas K.P., Department of Computer Science and Engineering, The National Institute of Engineering, Mysore, India Description An account of the resource Breast cancer recurrence is one of the most significant medical concern, and accurate recurrence models can assist in early intervention and treatment planning. Breast cancer recurrent remains as one of the most critical concern for patients prognosis and treatment planning. Accuracy Predicting individual recurrence risk is crucial for the development of precise therapy, specialy for those patients with high-risk profiles. In the study proposes a hybrid machine learning approach that uses the computational modeling and the medical information to predict the recurrence of breast cancer in a patient. The dataset contains the medical and patient information like the age, tumor size, lymph node involvement, malignancy degree, location, irradiation status and recurrence class. This proposed approach begins with the process of data processing, handling the missing data values, features normalization and encoding of categorical variable into numerical format. The dataset is divided into two parts the training set and the testing set and the two selected models random forest and logistic regression models are trained independently. The predictions form both the model is stacked and a logistic regression meta-model is trained on these combined predictions. The evaluation of the model was conducted using the metrics such as accuracy, precision, recall, and F1 score. The designed hybrid model was able to achieve the accuracy of 97.66% with the precision, recall and F1 score all reaching around 98.15%. This study highlights the potential of hybrid machine learning techniques, improving the accuracy and reliability of machine learning models for breast cancer recurrence prediction. This development model can serve as a valuable tool for the medical industry to support decision making and assist in personalized treatment decisions, offering early detection of recurrence. This can enhance the treatment of a patient by supporting early detection and patients outcomes through targeted therapy. Copyright 2026 Techno-Press Subject The topic of the resource breast cancer; logistic regression; machine learning; random forest; recurrence Publisher An entity responsible for making the resource available Techno-Press Relation A related resource ISSN: 23838477; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online