{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T06:16:27Z","timestamp":1778220987146,"version":"3.51.4"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":37,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-026-01190-7","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T15:24:16Z","timestamp":1775057056000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HybGANN as an interpretable generative evolutionary model for predicting diabetes from imbalanced data"],"prefix":"10.1007","volume":"6","author":[{"given":"Nora","family":"Pireci Sejdiu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Blagoj","family":"Ristevski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kostandina","family":"Veljanovska","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikola","family":"Rendevski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azir","family":"Aliu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"issue":"10","key":"1190_CR1","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/s10462-024-10884-2","volume":"57","author":"M Salmi","year":"2024","unstructured":"Salmi M, Atif D, Oliva D. Ajith Abraham, and Sebastian Ventura. Handling imbalanced medical datasets: review of a decade of research. Artif Intell Rev. 2024;57(10):273.","journal-title":"Artif Intell Rev"},{"issue":"3","key":"1190_CR2","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1049\/htl2.12010","volume":"8","author":"J Ramesh","year":"2021","unstructured":"Ramesh J, Aburukba R, Sagahyroon A. A remote healthcare monitoring framework for diabetes prediction using machine learning. Healthc Technol Lett. 2021;8(3):45\u201357.","journal-title":"Healthc Technol Lett"},{"key":"1190_CR3","doi-asserted-by":"crossref","unstructured":"Sampson JR. Adaptation in natural and artificial systems (John H. Holland). (1976): 529.","DOI":"10.1137\/1018105"},{"issue":"1","key":"1190_CR4","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn Res. 2012;13(1):281\u2013305.","journal-title":"J Mach Learn Res"},{"key":"1190_CR5","doi-asserted-by":"publisher","first-page":"100032","DOI":"10.1016\/j.cmpbup.2021.100032","volume":"1","author":"P Rajendra","year":"2021","unstructured":"Rajendra P. Prediction of diabetes using logistic regression and ensemble techniques. Comput Methods Programs Biomed Update. 2021;1:100032.","journal-title":"Comput Methods Programs Biomed Update"},{"key":"1190_CR6","unstructured":"Azad C, Bhushan B, Sharma R, Shankar A, Singh KK, and Aditya Khamparia. Prediction model using SMOTE, genetic algorithmdecision tree (PMSGD) for classification of diabetes mellitus. Multimedia Syst (2022): 1\u201319."},{"key":"1190_CR7","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Kevin W, Bowyer LO, Hall. Philip Kegelmeyer. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321\u201357.","journal-title":"J Artif Intell Res"},{"key":"1190_CR8","doi-asserted-by":"crossref","unstructured":"Figueira A, Vaz B. Survey on synthetic data generation, evaluation methods and GANs. Mathematics 10, no. 15 (2022): 2733.","DOI":"10.3390\/math10152733"},{"issue":"1","key":"1190_CR9","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1186\/s40537-022-00648-6","volume":"9","author":"Sauber-Cole","year":"2022","unstructured":"Sauber-Cole, Rick, Taghi M. Khoshgoftaar. The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey. J Big Data. 2022;9(1):98.","journal-title":"J Big Data"},{"key":"1190_CR10","doi-asserted-by":"crossref","unstructured":"Bourou S, Saer AE, Velivassaki T-H. Artemis Voulkidis, and Theodore Zahariadis. A review of tabular data synthesis using GANs on an IDS dataset. Information 12, no. 09 (2021): 375.","DOI":"10.3390\/info12090375"},{"key":"1190_CR11","unstructured":"Borisov V, Leemann T, Se\u221a\u00fcler K, Haug J, Pawelczyk M, and Gjergji Kasneci. Deep neural networkstabular data: A survey. IEEE transactions on neural networkslearning systems (2022)."},{"key":"1190_CR12","unstructured":"Xu, Lei M, Skoularidou A, Cuesta-Infante, Veeramachaneni K. Modeling tabular data using conditional gan. arXiv 2019. arXiv preprint arXiv:1907.00503 1 (2019)."},{"key":"1190_CR13","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Aaron C. Courville. Improved training of wasserstein gans. Advances in neural information processing systems 30 (2017)."},{"issue":"2","key":"1190_CR14","doi-asserted-by":"publisher","first-page":"488","DOI":"10.3390\/make4020022","volume":"4","author":"A Rajabi","year":"2022","unstructured":"Rajabi A. Tabfairgan: Fair tabular data generation with generative adversarial networks. Mach Learn Knowl Extr. 2022;4(2):488\u2013501.","journal-title":"Mach Learn Knowl Extr"},{"issue":"5","key":"1190_CR15","first-page":"8927","volume":"40","author":"SM Iranmanesh","year":"2021","unstructured":"Iranmanesh SM, Nasser M. Nasrabadi. HGAN: Hybrid generative adversarial network. J Intell Fuzzy Syst. 2021;40(5):8927\u201338.","journal-title":"J Intell Fuzzy Syst"},{"key":"1190_CR16","doi-asserted-by":"publisher","first-page":"123533","DOI":"10.1016\/j.eswa.2024.123533","volume":"249","author":"RG Gayathri","year":"2024","unstructured":"Gayathri RG, Sajjanhar A, Xiang Y. Hybrid deep learning model using spcagan augmentation for insider threat analysis. Expert Syst Appl. 2024;249:123533.","journal-title":"Expert Syst Appl"},{"key":"1190_CR17","unstructured":"Brock A, Donahue J, and Karen Simonyan. Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:180911096 (2018)."},{"issue":"5","key":"1190_CR18","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s11063-024-11636-7","volume":"56","author":"T\u00fcmay Ate\u015f","year":"2024","unstructured":"T\u00fcmay Ate\u015f. K\u00fcbra, \u0130brahim Erdem Kalkan, and Cenk \u015eahin. Training Artificial Neural Network with a Cultural Algorithm. Neural Process Lett. 2024;56(5):225.","journal-title":"Neural Process Lett"},{"key":"1190_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.12720\/jait.13.1.95-99","volume":"13","author":"J Arroyo","year":"2022","unstructured":"Arroyo J, Carlo T, Allemar Jhone P, Delima. An optimized neural network using genetic algorithm for cardiovascular disease prediction. J Adv Inform Technol. 2022;13:1.","journal-title":"J Adv Inform Technol"},{"key":"1190_CR20","doi-asserted-by":"crossref","unstructured":"Felizardo V, Garcia NM, Pombo N, Imen Megdiche. Artif Intell Med. 2021;118:102120. Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction-a systematic literature review.","DOI":"10.1016\/j.artmed.2021.102120"},{"key":"1190_CR21","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1007\/978-3-642-04898-2_455","volume-title":"International encyclopedia of statistical science","author":"I Jolliffe","year":"2011","unstructured":"Jolliffe I. Principal component analysis. International encyclopedia of statistical science. Berlin, Heidelberg: Springer; 2011. pp. 1094\u20136."},{"key":"1190_CR22","doi-asserted-by":"publisher","unstructured":"Lin J. Divergence measures based on the Shannon entropy, in IEEE Transactions on Information Theory, vol. 37, no. 1, pp. 145\u2013151, Jan. 1991, https:\/\/doi.org\/10.1109\/18.61115","DOI":"10.1109\/18.61115"},{"key":"1190_CR23","unstructured":"Lundberg SM, Su-In. Lee. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 30 (2017)."},{"issue":"1","key":"1190_CR24","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R. Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2(1):56\u201367.","journal-title":"Nat Mach Intell"},{"key":"1190_CR25","doi-asserted-by":"crossref","unstructured":"Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd international conference on Machine learning, pp. 233\u2013240. 2006.","DOI":"10.1145\/1143844.1143874"},{"key":"1190_CR26","doi-asserted-by":"crossref","unstructured":"Fawcett T. An introduction to ROC analysis. Pattern recognition letters 27, 8 (2006): 861\u201374.","DOI":"10.1016\/j.patrec.2005.10.010"},{"key":"1190_CR27","doi-asserted-by":"crossref","unstructured":"Yao X. Evolving artificial neural networks. Proceedings of the IEEE 87, no. 9 (1999): 1423\u20131447.","DOI":"10.1109\/5.784219"},{"key":"1190_CR28","doi-asserted-by":"crossref","unstructured":"Singh V, Asari VK, Rajasekaran R. A deep neural network for early detection and prediction of chronic kidney disease. Diagnostics 12, no. 1 (2022): 116.","DOI":"10.3390\/diagnostics12010116"},{"issue":"1","key":"1190_CR29","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1186\/s12859-023-05467-x","volume":"24","author":"Y Feng, Xin","year":"2023","unstructured":"Feng, Xin Y, Cai, Xin R. Optimizing diabetes classification with a machine learning-based framework. BMC Bioinformatics. 2023;24(1):428.","journal-title":"BMC Bioinformatics"},{"key":"1190_CR30","doi-asserted-by":"publisher","first-page":"32","DOI":"10.52756\/ijerr.2023.v30.004","volume":"30","author":"S Jaiswal","year":"2023","unstructured":"Jaiswal S, Gupta P. GLSTM: a novel approach for prediction of real & synthetic PID diabetes data using GANs and LSTM classification model. Int J Exp Res Rev. 2023;30:32\u201345.","journal-title":"Int J Exp Res Rev"},{"key":"1190_CR31","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/s20144036","volume":"20","author":"S Mishra","year":"2020","unstructured":"Mishra S, Tripathy HK, Mallick PK, Bhoi AK, Barsocchi P. EAGA-MLP-an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors. 2020;20:14.","journal-title":"Sensors"},{"issue":"4","key":"1190_CR32","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1002\/for.2652","volume":"39","author":"Pekel","year":"2020","unstructured":"Pekel, \u00d6zmen. Ebru, and Tuncay \u00d6zcan. Diagnosis of diabetes mellitus using artificial neural network and classification and regression tree optimized with genetic algorithm. J Forecast. 2020;39(4):661\u201370.","journal-title":"J Forecast"},{"key":"1190_CR33","doi-asserted-by":"crossref","unstructured":"Vu L, Quang Uy Nguyen. Handling imbalanced data in intrusion detection systems using generative adversarial networks. J Res Develop Info CommunTechnol. 2020,1:1\u201313.","DOI":"10.32913\/mic-ict-research.v2020.n1.894"},{"key":"1190_CR34","doi-asserted-by":"publisher","first-page":"104540","DOI":"10.1016\/j.compbiomed.2021.104540","volume":"135","author":"Y Xiao","year":"2021","unstructured":"Xiao Y, Wu J, Lin Z. Cancer diagnosis using generative adversarial networks based on deep learning from imbalanced data. Comput Biol Med. 2021;135:104540.","journal-title":"Comput Biol Med"},{"key":"1190_CR35","unstructured":"Teboul A. Diabetes Health Indicators Dataset. Kaggle. 2021 https:\/\/www.kaggle.com\/datasets\/alexteboul\/diabetes-health-indicators-dataset"},{"issue":"1","key":"1190_CR36","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1186\/s12859-023-05443-5","volume":"24","author":"P\u0131nar Ata\u015f","year":"2023","unstructured":"Ata\u015f P\u0131nar. A novel hybrid model to predict concomitant diseases for Hashimoto\u2019s thyroiditis. BMC Bioinformatics. 2023;24(1):319.","journal-title":"BMC Bioinformatics"},{"key":"1190_CR37","doi-asserted-by":"publisher","unstructured":"Karaday\u0131 Ata\u015f P\u0131nar. 2024. Exploring the Molecular Interaction of PCOS and Endometrial Carcinoma through Novel Hyperparameter-Optimized Ensemble Clustering Approaches Mathematics 12, no. 2: 295. https:\/\/doi.org\/10.3390\/math12020295","DOI":"10.3390\/math12020295"},{"issue":"9","key":"1190_CR38","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.1007\/s11590-021-01816-y","volume":"17","author":"P Karadayi-Ata\u015f","year":"2023","unstructured":"Karadayi-Ata\u015f P, Sevkli AZ, Tufan K. A VNS based framework for early diagnosis of the Alzheimer\u2019s disease converted from mild cognitive impairment. Optimisation Lett. 2023;17(9):2347\u201366.","journal-title":"Optimisation Lett"},{"issue":"7","key":"1190_CR39","doi-asserted-by":"publisher","first-page":"3357","DOI":"10.1007\/s00521-023-09031-9","volume":"36","author":"P\u0131nar Ata\u015f","year":"2024","unstructured":"Ata\u015f P\u0131nar. Evaluate student achievement by classifying brain structure and its functionality with novel hybrid method. Neural Comput Appl. 2024;36(7):3357\u201368.","journal-title":"Neural Comput Appl"},{"issue":"1","key":"1190_CR40","first-page":"1","volume":"13","author":"Karaday\u0131 Ata\u015f","year":"2025","unstructured":"Karaday\u0131 Ata\u015f. P\u0131nar. A novel clustered-based binary grey wolf optimizer to solve the feature selection problem for uncovering the genetic links between non-Hodgkin lymphomas and rheumatologic diseases. Health Inform Sci Syst. 2025;13(1):1\u201322.","journal-title":"Health Inform Sci Syst"},{"key":"1190_CR41","doi-asserted-by":"publisher","first-page":"110343","DOI":"10.1016\/j.compbiomed.2025.110343","volume":"193","author":"P\u0131nar Ata\u015f","year":"2025","unstructured":"Ata\u015f P\u0131nar. A novel Harris Hawks Optimization-based clustering method for elucidating genetic associations in osteoarthritis and Diverse Cancer Types. Comput Biol Med. 2025;193:110343.","journal-title":"Comput Biol Med"},{"issue":"1","key":"1190_CR42","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s13755-025-00396-w","volume":"14","author":"C-T Dao","year":"2025","unstructured":"Dao C-T, Phan NMT, Ding J-E, Wu C, Restrepo D, Luo D, Zhao F, et al. CURENet: combining unified representations for efficient chronic disease prediction. Health Inform Sci Syst. 2025;14(1):7.","journal-title":"Health Inform Sci Syst"},{"key":"1190_CR43","doi-asserted-by":"crossref","unstructured":"Li N, Xue B, Ma L, Zhang M. Transferable Relativistic Predictor: Mitigating Cross-Task Cold-Start Issue in NAS. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, pp. 5625\u20135633. 2025.","DOI":"10.24963\/ijcai.2025\/626"},{"issue":"1","key":"1190_CR44","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1186\/s12859-025-06101-8","volume":"26","author":"S Khan","year":"2025","unstructured":"Khan S, Noor S, Awan HH, Iqbal S, AlQahtani SA. Naqqash Dilshad, and Nijad Ahmad. Deep-ProBind: binding protein prediction with transformer-based deep learning model. BMC Bioinformatics. 2025;26(1):88.","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"1190_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12915-025-02433-2","volume":"23","author":"N Almusallam","year":"2025","unstructured":"Almusallam N, Khan S, Alarfaj FK, Ahmad N. A robust deep learning framework for RNA 5-methyluridine modification prediction using integrated features. BMC Biol. 2025;23(1):1\u201315.","journal-title":"BMC Biol"},{"key":"1190_CR46","doi-asserted-by":"crossref","unstructured":"Noor S, AlQahtani SA. Train Model Computers Mater Continua. 2025;83:1. and Salman Khan. XGBoost-Liver: An Intelligent Integrated Features Approach for Classifying Liver Diseases Using Ensemble XGBoost.","DOI":"10.32604\/cmc.2025.061700"},{"issue":"1","key":"1190_CR47","doi-asserted-by":"publisher","first-page":"35872","DOI":"10.1038\/s41598-025-19689-x","volume":"15","author":"S Khan","year":"2025","unstructured":"Khan S, Dilshad N, Ahmad N, Noor S, Salman A. AlQahtani. Integrating AI in security information and event management for real time cyber defense. Sci Rep. 2025;15(1):35872.","journal-title":"Sci Rep"},{"key":"1190_CR48","doi-asserted-by":"publisher","first-page":"101956","DOI":"10.1016\/j.swevo.2025.101956","volume":"96","author":"N Li","year":"2025","unstructured":"Li N, Ma L, Wang R, Cheng S, Sun Y. Bing Xue, and Mengjie Zhang. Listwise ranking predictor for evolutionary neural architecture search. Swarm Evol Comput. 2025;96:101956.","journal-title":"Swarm Evol Comput"},{"key":"1190_CR49","doi-asserted-by":"crossref","unstructured":"Li N, Xue B, Ma L, Zhang M. Automatic fuzzy architecture design for defect detection via classifier-assisted multiobjective optimization approach. IEEE Trans Evol Comput (2025).","DOI":"10.1109\/TEVC.2025.3530416"},{"key":"1190_CR50","doi-asserted-by":"crossref","unstructured":"Mei A, Li N, Ma L, Gao K, Huang M, Xue B. and Yaochu Jin. Binary-to-decimal encoding-based performance predictor for evolutionary graph fusion architecture search and applications to medical data. IEEE Trans Evol Comput (2025).","DOI":"10.1109\/TEVC.2025.3639127"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-026-01190-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-026-01190-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-026-01190-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T06:00:23Z","timestamp":1778220023000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-026-01190-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,1]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1190"],"URL":"https:\/\/doi.org\/10.1007\/s44163-026-01190-7","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,1]]},"assertion":[{"value":"10 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The author(s) consent to publish the articles.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial number"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"419"}}