{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:59:20Z","timestamp":1777705160479,"version":"3.51.4"},"reference-count":28,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,4,18]]},"abstract":"<jats:p>Breast cancer has been life-threatening for many years as it is the common cause of fatality among women. The challenges of screening such tumors through manual approaches can be overcome by computer-aided diagnosis, which aids radiologists in making precise decisions. The selection of significant features is crucial for the estimation of prediction accuracy. This work proposes a hybrid Genetic Algorithm (GA) and Honey Badger Algorithm (HBA) based Deep Neural Network (DNN), HGAHBA-DNN for the concurrent optimal features selection and parameter optimization; further, the optimal features and parameters extracted are fed into the DNN for the prediction of the breast cancer. It fuses the benefits of HBA with parallel processing and efficient feedback with GA\u2019s excellent global convergent rate during the processing stages. The aforementioned method is evaluated on the Wisconsin Original Breast Cancer (WOBC), Wisconsin Diagnostic Breast Cancer (WDBC), and the Surveillance, Epidemiology, and End Results (SEER) datasets. Subsequently, the performance is validated using several metrics like accuracy, precision, Recall, and F1-score. The experimental result shows that HGAHBA-DNN obtains accuracy of 99.42%, 99.84%, and 92.44% for the WOBC, WDBC, and SEER datasets respectively, which is much superior to the other state-of-the-art methods.<\/jats:p>","DOI":"10.3233\/jifs-236577","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T10:44:29Z","timestamp":1708080269000},"page":"8037-8048","source":"Crossref","is-referenced-by-count":3,"title":["Optimizing feature selection and parameter tuning for breast cancer detection using hybrid GAHBA-DNN framework"],"prefix":"10.1177","volume":"46","author":[{"given":"K.","family":"Kamala Devi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamilnadu, India"}]},{"given":"J.","family":"Raja Sekar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamilnadu, India"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-236577_ref1","first-page":"23","article-title":"Diversity of Breast Carcinoma: Histological Subtypes and Clinical Relevance","volume":"8","author":"Makki","year":"2015","journal-title":"Clin Med Insights Pathol"},{"issue":"6","key":"10.3233\/JIFS-236577_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42452-019-0645-7","article-title":"Evaluation of machine learning-based optimized feature selection approaches and classification methods for cervical cancer prediction","volume":"1","author":"Nithya","year":"2019","journal-title":"SN Appl Sci"},{"issue":"2","key":"10.3233\/JIFS-236577_ref3","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1142\/S219688882150007X","article-title":"Breast cancer detection based on feature selection using enhanced grey wolf optimizer and support vector machine algorithms","volume":"8","author":"Kumar","year":"2021","journal-title":"Vietnam Journal of Computer Science"},{"issue":"1","key":"10.3233\/JIFS-236577_ref4","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1007\/s00521-015-1920-1","article-title":"Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems","volume":"27","author":"Mirjalili","year":"2016","journal-title":"Neural computing and applications"},{"issue":"6","key":"10.3233\/JIFS-236577_ref5","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1142\/S0218488521500380","article-title":"Surveil and Prediction of Pandemic Disease by Fine-Tuning Hyperparameters in Deep Learning","volume":"29","author":"Shanthini","year":"2021","journal-title":"International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems"},{"key":"10.3233\/JIFS-236577_ref6","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/978-3-319-93025-1_4","article-title":"Genetic algorithm","author":"Mirjalili","year":"2019","journal-title":"Evolutionary Algorithms and Neural Networks: Theory and Applications"},{"issue":"2","key":"10.3233\/JIFS-236577_ref7","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s10489-017-0967-3","article-title":"Improved monarchs butterfly optimization for unconstrained global search and neural network training","volume":"48","author":"Faris","year":"2017","journal-title":"Appl. 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