{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:04:36Z","timestamp":1755219876618,"version":"3.43.0"},"reference-count":0,"publisher":"SASA Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JOWUA"],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>Leukemia classification involves identification and categorization of various leukemias, a cluster of\nblood malignancies influencing white blood cells. Proper classification is crucial for selecting the\nappropriate treatment modalities and predicting outcomes in patients. Historically, leukemia\nclassification was based on clinical and morphological characteristics, but new developments in\ngenomics like microarray and next-generation sequencing tools have facilitated more accurate\nmolecular classifications. Machine learning (ML) and deep learning (DL) methods have transformed\nleukemia classification by enabling automation of analysis in large and intricate datasets to ensure\nmore accurate and efficient leukemia subtype classification. The primary goal of this research is to\nsuggest a new leukemia classification method using microarray data. Leukemia microarray data first\nundergoes preprocessing, after which feature selection is performed through Serial ExponentialSecretary Bird Optimization Algorithm (SE-SBOA). SE-SBOA is an optimization method that\nembeds the exponential weighted moving average concept (EWMA) into Secretary Bird\nOptimization Algorithm (SBOA). The method helps to find the best feature subset, improving model\nperformance at lower complexity. Lastly, leukemia classification is done using the proposed\nensemble method that combines Graph Neural Network (GNN), Multi-Layer Perceptron (MLP) and\nRandom Forest. Utilizing the advantages of GNN, MLP and Random Forest, the model proposed\nherein attains higher classification accuracy and proves to outperform traditional methods.\nExperimental results demonstrate that the SE-SBOA-based Ensemble Learning technique\noutperformed standard methods, attaining an accuracy of 95.9%, a precision of 96.1%, a recall of\n96.2%, and an F1-score of 96.2%.<\/jats:p>","DOI":"10.58346\/jowua.2025.i2.056","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T12:26:06Z","timestamp":1754569566000},"page":"903-926","source":"Crossref","is-referenced-by-count":0,"title":["Optimization Enabled Ensemble Learning for Leukemia Classification Using Microarray Data"],"prefix":"10.58346","volume":"16","author":[{"given":"P.C.","family":"Chaitra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dr.R. Saravana","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"37075","published-online":{"date-parts":[[2025,6,30]]},"container-title":["Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications"],"original-title":[],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T12:26:10Z","timestamp":1754569570000},"score":1,"resource":{"primary":{"URL":"https:\/\/jowua.com\/wp-content\/uploads\/2025\/08\/2025.I2.056.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,6,30]]},"published-print":{"date-parts":[[2025,6,30]]}},"URL":"https:\/\/doi.org\/10.58346\/jowua.2025.i2.056","relation":{},"ISSN":["2093-5374","2093-5382"],"issn-type":[{"value":"2093-5374","type":"print"},{"value":"2093-5382","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,30]]}}}