{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:41:54Z","timestamp":1781282514514,"version":"3.54.1"},"reference-count":29,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T00:00:00Z","timestamp":1757462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Dengue fever is a mosquito-borne viral disease that annually affects 100\u2013400 million people worldwide. Early detection of dengue enables easy treatment planning and helps reduce mortality rates. This study proposes three Swarm-based Metaheuristic Algorithms, Golden Jackal Optimization, Fox Optimizer, and Sea Lion Optimization, for feature selection and hyperparameter tuning, and an Extreme Gradient Boost classifier to forecast dengue fever using the Predictive Clinical Dengue dataset. Several existing models have been proposed for dengue fever classification, with some achieving high predictive performance. However, most of these studies have overlooked the importance of feature reduction, which is crucial to building efficient and interpretable models. Furthermore, prior research has lacked in-depth analysis of model behavior, particularly regarding the underlying causes of misclassification. Addressing these limitations, this study achieved a 10-fold cross-validation mean accuracy of 99.89%, an F-score of 99.92%, a precision of 99.84%, and a perfect recall of 100% by using only two features: WBC Count and Platelet Count. Notably, FOX-XGBoost and SLO-XGBoost achieved the same performance while utilizing only four and three features, respectively, demonstrating the effectiveness of feature reduction without compromising accuracy. Among these, GJO-XGBoost demonstrated the most efficient feature utilization while maintaining superior performance, emphasizing its potential for practical deployment in dengue fever diagnosis. SHAP analysis identified WBC Count as the most influential feature driving model predictions. Furthermore, DiCE explanations support this finding by showing that lower WBC Counts are associated with dengue-positive cases, whereas higher WBC Counts are indicative of dengue-negative individuals. SHAP interpreted the reasons behind misclassifications, while DiCE provided a correction mechanism by suggesting the minimal changes needed to convert incorrect predictions into correct ones.<\/jats:p>","DOI":"10.3390\/info16090789","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T14:14:55Z","timestamp":1757513695000},"page":"789","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dengue Fever Detection Using Swarm Intelligence and XGBoost Classifier: An Interpretable Approach with SHAP and DiCE"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9502-2152","authenticated-orcid":false,"given":"Proshenjit","family":"Sarker","sequence":"first","affiliation":[{"name":"Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1178-9356","authenticated-orcid":false,"given":"Jun-Jiat","family":"Tiang","sequence":"additional","affiliation":[{"name":"Centre for Wireless Technology, CoE for Intelligent Network, Faculty of Artificial Intelligence & Engineering, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah-Al","family":"Nahid","sequence":"additional","affiliation":[{"name":"Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,10]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2025, July 15). Dengue and Severe Dengue. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/dengue-and-severe-dengue."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Damtew, Y.T., Tong, M., Varghese, B.M., Anikeeva, O., Hansen, A., Dear, K., Zhang, Y., Morgan, G., Driscoll, T., and Bi, P. (2023). Effects of high temperatures and heatwaves on dengue fever: A systematic review and meta-analysis. EBioMedicine, 91.","DOI":"10.1016\/j.ebiom.2023.104582"},{"key":"ref_3","unstructured":"Pan American Health Organization (2025, July 15). Dengue Indicators. Available online: https:\/\/www3.paho.org\/data\/index.php\/en\/mnu-topics\/indicadores-dengue-en.html."},{"key":"ref_4","unstructured":"World Health Organization (2025, May 13). Global Dengue Dashboard. Available online: https:\/\/worldhealthorg.shinyapps.io\/dengue_global\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1038\/nature12060","article-title":"The global distribution and burden of dengue","volume":"496","author":"Bhatt","year":"2013","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"257","DOI":"10.18196\/jrc.v3i3.14387","article-title":"Early diagnosis for dengue disease prediction using efficient machine learning techniques based on clinical data","volume":"3","author":"Abdualgalil","year":"2022","journal-title":"J. Robot. Control"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.procs.2020.12.018","article-title":"Evaluation of dengue model performances developed using artificial neural network and random forest classifiers","volume":"179","author":"Silitonga","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_8","first-page":"3881","article-title":"Early detection of dengue using machine learning algorithms","volume":"118","author":"Rajathi","year":"2018","journal-title":"Int. J. Pure Appl. Math."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.nhtm.2017.10.001","article-title":"PSO-ANN based diagnostic model for the early detection of dengue disease","volume":"4","author":"Gambhir","year":"2017","journal-title":"New Horizons Transl. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1186\/s12879-024-09220-4","article-title":"Improving dengue fever predictions in Taiwan based on feature selection and random forests","volume":"24","author":"Kuo","year":"2024","journal-title":"BMC Infect. Dis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"363","DOI":"10.31449\/inf.v43i3.1548","article-title":"Machine learning for dengue outbreak prediction: A performance evaluation of different prominent classifiers","volume":"43","author":"Iqbal","year":"2019","journal-title":"Informatica"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"112355","DOI":"10.1109\/ACCESS.2024.3443299","article-title":"Artificial intelligence based early detection of dengue using CBC data","volume":"12","author":"Riya","year":"2024","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chowdhury, S.U., Sayeed, S., Rashid, I., Alam, M.G.R., Masum, A.K.M., and Dewan, M.A.A. (2022). Shapley-additive-explanations-based factor analysis for dengue severity prediction using machine learning. J. Imaging, 8.","DOI":"10.3390\/jimaging8090229"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.11591\/ijai.v11.i3.pp1119-1129","article-title":"Dengue classification method using support vector machines and cross-validation techniques","volume":"11","author":"Hamdani","year":"2022","journal-title":"IAES Int. J. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e361","DOI":"10.1016\/S2214-109X(22)00514-9","article-title":"Early diagnostic indicators of dengue versus other febrile illnesses in Asia and Latin America (IDAMS study): A multicentre, prospective, observational study","volume":"11","author":"Rosenberger","year":"2023","journal-title":"Lancet Glob. Health"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111030","DOI":"10.1016\/j.dib.2024.111030","article-title":"A benchmark dataset for analyzing hematological responses to dengue fever in Bangladesh","volume":"57","author":"Islam","year":"2024","journal-title":"Data Brief"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e16492","DOI":"10.2196\/16492","article-title":"Analyzing medical research results based on synthetic data and their relation to real data results: Systematic comparison from five observational studies","volume":"8","author":"Benaim","year":"2020","journal-title":"JMIR Med. Inform."},{"key":"ref_18","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1093\/comjnl\/14.4.422","article-title":"An algorithm with guaranteed convergence for finding a zero of a function","volume":"14","author":"Brent","year":"1971","journal-title":"Comput. J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sarker, P., Tiang, J.J., and Nahid, A.A. (2025). Metaheuristic-Driven Feature Selection for Human Activity Recognition on KU-HAR Dataset Using XGBoost Classifier. Sensors, 25.","DOI":"10.3390\/s25175303"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"26766","DOI":"10.1109\/ACCESS.2021.3056407","article-title":"Metaheuristic algorithms on feature selection: A survey of one decade of research (2009\u20132019)","volume":"9","author":"Agrawal","year":"2021","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"116924","DOI":"10.1016\/j.eswa.2022.116924","article-title":"Golden jackal optimization: A novel nature-inspired optimizer for engineering applications","volume":"198","author":"Chopra","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1007\/s10489-022-03533-0","article-title":"FOX: A FOX-inspired optimization algorithm","volume":"53","author":"Mohammed","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_25","first-page":"388","article-title":"Sea lion optimization algorithm","volume":"10","author":"Masadeh","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_26","unstructured":"Lundberg, S.M., and Lee, S.I. (2017). A unified approach to interpreting model predictions. arXiv."},{"key":"ref_27","first-page":"307","article-title":"A value for n-person games","volume":"2","author":"Shapley","year":"1953","journal-title":"Contrib. Theory Games"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mothilal, R.K., Sharma, A., and Tan, C. (2020, January 27\u201330). Explaining machine learning classifiers through diverse counterfactual explanations. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Barcelona, Spain.","DOI":"10.1145\/3351095.3372850"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chaloemwong, J., Tantiworawit, A., Rattanathammethee, T., Hantrakool, S., Chai-Adisaksopha, C., Rattarittamrong, E., and Norasetthada, L. (2018). Useful clinical features and hematological parameters for the diagnosis of dengue infection in patients with acute febrile illness: A retrospective study. BMC Hematol., 18.","DOI":"10.1186\/s12878-018-0116-1"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/9\/789\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:43:28Z","timestamp":1760035408000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/9\/789"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,10]]},"references-count":29,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["info16090789"],"URL":"https:\/\/doi.org\/10.3390\/info16090789","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,10]]}}}