{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T02:57:22Z","timestamp":1790477842840,"version":"4.1.0"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-10689-5","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T09:24:10Z","timestamp":1760606650000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Enhancing explainability in epidemiological predictions using fuzzy logic integrated with machine and deep learning algorithms"],"prefix":"10.1038","volume":"15","author":[{"given":"Ubaida","family":"Fatima","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rabia","family":"Khushal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"10689_CR1","unstructured":"Merrill, R. M. Introduction to Epidemiology (Jones & Bartlett Learning)."},{"issue":"11","key":"10689_CR2","doi-asserted-by":"publisher","first-page":"3475","DOI":"10.15829\/1728-8800-2022-3475","volume":"21","author":"SA Shalnova","year":"2022","unstructured":"Shalnova, S. A. et al. How to interpret and use the results of epidemiological studies in healthcare practice. Methodological Rationale. Cardiovasc. Ther. Prev. 21(11), 3475 (2022).","journal-title":"Cardiovasc. Ther. Prev."},{"issue":"8","key":"10689_CR3","doi-asserted-by":"publisher","first-page":"943","DOI":"10.3390\/sym16080943","volume":"16","author":"A Bouchnita","year":"2024","unstructured":"Bouchnita, A. & Djafari-Rouhani, B. Integrating genomic, climatic, and immunological factors to analyze seasonal patterns of influenza variants. Symmetry. 16(8), 943 (2024).","journal-title":"Symmetry."},{"key":"10689_CR4","doi-asserted-by":"publisher","first-page":"103894","DOI":"10.1016\/j.jbi.2021.103894","volume":"122","author":"L Li","year":"2021","unstructured":"Li, L., Jiang, Y. & Huang, B. Long-term prediction for temporal propagation of seasonal influenza using transformer-based model. J. Biomed. Inform. 122, 103894 (2021).","journal-title":"J. Biomed. Inform."},{"issue":"1","key":"10689_CR5","doi-asserted-by":"publisher","first-page":"331","DOI":"10.32604\/cmc.2020.012148","volume":"66","author":"MA Khan","year":"2021","unstructured":"Khan, M. A. et al. Forecast the influenza pandemic using machine learning. Comput. Mater. Continua 66(1), 331\u2013340 (2021).","journal-title":"Comput. Mater. Continua"},{"issue":"2","key":"10689_CR6","doi-asserted-by":"publisher","first-page":"e1491","DOI":"10.1002\/cti2.1491","volume":"13","author":"SL Leong","year":"2024","unstructured":"Leong, S. L., Gras, S. & Grant, E. J. Fighting flu: novel CD8+ T-cell targets are required for future influenza vaccines. Clin. Transl. Immunol 13(2), e1491 (2024).","journal-title":"Clin. Transl. Immunol"},{"key":"10689_CR7","unstructured":"H1N1 flu (swine flu). https:\/\/www.mayoclinic.org\/diseases-conditions\/swine-flu\/symptoms-causes\/syc-20378103. (Accessed Mar 2023)."},{"key":"10689_CR8","doi-asserted-by":"crossref","unstructured":"Madavi, M. et al. Reviewing the pillars of immunization: essential vaccines and their. Int. J. Pharm. Qual. Assur. 15(3) (2024).","DOI":"10.25258\/ijpqa.15.3.96"},{"key":"10689_CR9","doi-asserted-by":"crossref","unstructured":"Zavaleta-Monestel, E. et al. The impact of vaccination as a strategy to combat bacterial antimicrobial resistance. Cureus. 16(7) (2024).","DOI":"10.7759\/cureus.65840"},{"issue":"26","key":"10689_CR10","doi-asserted-by":"publisher","first-page":"126408","DOI":"10.1016\/j.vaccine.2024.126408","volume":"42","author":"J Taaffe","year":"2024","unstructured":"Taaffe, J. et al. Advancing influenza vaccines: A review of next-generation candidates and their potential for global health impact. Vaccine 42(26), 126408 (2024).","journal-title":"Vaccine"},{"key":"10689_CR11","unstructured":"Kodage, P. B. et al. Covid-19 a comprehensive review of signs, symptoms, diagnosis, and treatment strategies. Int. J. Adv. Res. Sci. Commun. Technol. (2024)."},{"key":"10689_CR12","doi-asserted-by":"crossref","unstructured":"McMillen, C. W. COVID-19. In Pandemics: A Very Short Introduction. (Oxford Academic, 2024).","DOI":"10.1093\/actrade\/9780197762004.001.0001"},{"issue":"14","key":"10689_CR13","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1056\/NEJMra1208802","volume":"370","author":"HV Fineberg","year":"2014","unstructured":"Fineberg, H. V. Pandemic preparedness and response\u2014lessons from the H1N1 influenza of 2009. N. Engl. J. Med. 370(14), 1335\u20131342 (2014).","journal-title":"N. Engl. J. Med."},{"key":"10689_CR14","doi-asserted-by":"crossref","unstructured":"Kwok, W. M. et al. A time-stepper neural network model for the maximum likelihood estimation of epidemic parameters. In 2024 20th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), (2024).","DOI":"10.1109\/CSPA60979.2024.10525520"},{"key":"10689_CR15","doi-asserted-by":"publisher","first-page":"230","DOI":"10.21833\/ijaas.2024.05.025","volume":"115","author":"JI Janjua","year":"2024","unstructured":"Janjua, J. I. et al. Enhancing smart grid electricity prediction with the fusion of intelligent modeling and XAI integration. Int. J. Adv. Appl. Sci. 115, 230\u2013248 (2024).","journal-title":"Int. J. Adv. Appl. Sci."},{"key":"10689_CR16","doi-asserted-by":"crossref","unstructured":"Begum, S. et al. A novel approach to mitigate academic underachievement in higher education: Feature selection, classifier performance, and interpretability in predicting student performance. Int. J. Adv. Appl. Sci. 11(5) (2024).","DOI":"10.21833\/ijaas.2024.05.015"},{"issue":"12","key":"10689_CR17","doi-asserted-by":"publisher","first-page":"29","DOI":"10.21833\/ijaas.2023.12.004","volume":"10","author":"NG Goswami","year":"2023","unstructured":"Goswami, N. G., Karnad, S., Sampathila, N. & Muralidhar Bairy, G. Current trends of artificial intelligence and applications in digital pathology: A comprehensive review. Int. J. Adv. Appl. Sci. 10(12), 29 (2023).","journal-title":"Int. J. Adv. Appl. Sci."},{"key":"10689_CR18","unstructured":"Khwayyit, R. I., Suhhiem, M. H. Applying the fuzzy homotopy analysis method to solve fuzzy initial value problems with variable coefficients. Int. J. Adv. Appl. Sci. 11(4)."},{"key":"10689_CR19","doi-asserted-by":"crossref","unstructured":"Subbarayan, A. et al. A fuzzy logic-assisted framework for disease detection (2023).","DOI":"10.22541\/au.169648579.96233908\/v1"},{"key":"10689_CR20","doi-asserted-by":"publisher","first-page":"102278","DOI":"10.1016\/j.datak.2024.102278","volume":"151","author":"H Parveen","year":"2024","unstructured":"Parveen, H., Rizvi, S. W. & Boddu, R. S. Fuzzy-ontology based knowledge driven disease risk level prediction with optimization assisted ensemble classifier. Data Knowl. Eng. 151, 102278 (2024).","journal-title":"Data Knowl. Eng."},{"key":"10689_CR21","doi-asserted-by":"publisher","first-page":"108429","DOI":"10.1016\/j.compbiomed.2024.108429","volume":"174","author":"R Khushal","year":"2024","unstructured":"Khushal, R. & Fatima, U. Fuzzy machine learning logic utilization on hormonal imbalance dataset. Comput. Biol. Med. 174, 108429 (2024).","journal-title":"Comput. Biol. Med."},{"key":"10689_CR22","doi-asserted-by":"crossref","unstructured":"Mustaffa, N. A. et al. Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models. Int. J. Adv. Appl. Sci. 11(1) (2024).","DOI":"10.21833\/ijaas.2024.01.003"},{"issue":"2","key":"10689_CR23","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/axioms13020115","volume":"13","author":"AG Pakes","year":"2024","unstructured":"Pakes, A. G. A SIR epidemic model allowing recovery. Axioms 13(2), 115 (2024).","journal-title":"Axioms"},{"key":"10689_CR24","doi-asserted-by":"crossref","unstructured":"Armita, N. L., Harini, L. P. & Utari, I. A. Model Matematika Sir Pada Penyebaran Penyakit Covid-19 Dengan Efektivitas Vaksin. E-Jurnal Matematika. 13(1) (2024).","DOI":"10.24843\/MTK.2024.v13.i01.p439"},{"issue":"2","key":"10689_CR25","first-page":"51","volume":"7","author":"E Demir","year":"2024","unstructured":"Demir, E. & Vural, C. An SIR model of influenza with the effects of treatment and vaccination. J. Math. Sci. Model. 7(2), 51\u201359 (2024).","journal-title":"J. Math. Sci. Model."},{"key":"10689_CR26","doi-asserted-by":"crossref","unstructured":"Iparraguirre-Villanueva, O. & Cabanillas-Carbonell, M. Predictive analysis of vector-borne diseases through tabular classification of epidemiological data. Int. J. Online Biomed. Eng. 20(13) (2024).","DOI":"10.3991\/ijoe.v20i13.50437"},{"key":"10689_CR27","doi-asserted-by":"crossref","unstructured":"Ginting, R. U., Zarlis, M., Sihombing, P. & Efendi, S. Determining epidemiological patterns in disease identification using mathematical models on machine learning based multilayer structures. Math. Cybern. Appl. Aspects. 4(4) (2024).","DOI":"10.15587\/1729-4061.2024.310522"},{"key":"10689_CR28","doi-asserted-by":"crossref","unstructured":"Barapatre, H., Jangir, J., Bajpai, S., Chawla, B. & Keswani, G. Analytics of epidemiological data using machine learning models. Int. J. Next-Gener. Comput. 14(1) (2023).","DOI":"10.47164\/ijngc.v14i1.1014"},{"key":"10689_CR29","doi-asserted-by":"crossref","unstructured":"Luna-Benoso, B. Detection and risk assessment of COVID-19 through machine learning. Int. J. Adv. Appl. Sci. 11(1), (2024).","DOI":"10.21833\/ijaas.2024.01.025"},{"issue":"1","key":"10689_CR30","first-page":"5292830","volume":"2022","author":"SC Mpeshe","year":"2022","unstructured":"Mpeshe, S. C. Fuzzy SEIR epidemic model of amoebiasis infection in human. Adv. Fuzzy Syst. 2022(1), 5292830 (2022).","journal-title":"Adv. Fuzzy Syst."},{"key":"10689_CR31","doi-asserted-by":"crossref","unstructured":"Dayan, F. et al. Numerical investigation of malaria disease dynamics in fuzzy environment. Comput. Mater. Continua. 74(2).","DOI":"10.32604\/cmc.2023.033261"},{"issue":"5","key":"10689_CR32","doi-asserted-by":"publisher","first-page":"648","DOI":"10.3390\/math12050648","volume":"12","author":"A Kalampakas","year":"2024","unstructured":"Kalampakas, A., Samanta, S., Bera, J. & Das, K. C. A fuzzy logic inference model for the evaluation of the effect of extrinsic factors on the transmission of infectious diseases. Mathematics 12(5), 648 (2024).","journal-title":"Mathematics"},{"key":"10689_CR33","doi-asserted-by":"crossref","unstructured":"Khushal, R. & Fatima, U. Fuzzy computing in healthcare. In 2024 International Visualization, Informatics and Technology Conference (IVIT), Kuala Lumpur, (2024).","DOI":"10.1109\/IVIT62102.2024.10692652"},{"key":"10689_CR34","doi-asserted-by":"crossref","unstructured":"Khushal, R. & Fatima, U. Fuzzy logic and machine learning for diabetes risk prediction using modifiable factors. Int. J. Adv. Appl. Sci. 11(12) (2024).","DOI":"10.21833\/ijaas.2024.12.025"},{"key":"10689_CR35","doi-asserted-by":"publisher","first-page":"103152","DOI":"10.1016\/j.mex.2024.103152","volume":"14","author":"R Khushal","year":"2025","unstructured":"Khushal, R. & Fatima, U. Machine learning and fuzzy logic fusion approach for osteoporosis risk prediction. MethodX 14, 103152 (2025).","journal-title":"MethodX"},{"key":"10689_CR36","doi-asserted-by":"publisher","first-page":"109636","DOI":"10.1016\/j.compbiomed.2024.109636","volume":"186","author":"R Khushal","year":"2025","unstructured":"Khushal, R. & Fatima, U. A novel fuzzy three-valued logic computational framework in machine learning for medicine dataset. Comput. Biol. Med. 186, 109636 (2025).","journal-title":"Comput. Biol. Med."},{"key":"10689_CR37","doi-asserted-by":"crossref","unstructured":"Moiz, A., Fatima, U. & Haque, M. Z. A new framework for pinpointing crucial proteins in protein-protein interaction. IEEE Access. 12 (2024).","DOI":"10.1109\/ACCESS.2024.3437215"},{"issue":"1","key":"10689_CR38","doi-asserted-by":"publisher","first-page":"102008","DOI":"10.1016\/j.jocs.2023.102008","volume":"70","author":"U Fatima","year":"2023","unstructured":"Fatima, U., Hina, S. & Wasif, M. A novel global clustering coefficient-dependent degree centrality (GCCDC) metric for large network analysis using real-world datasets. J. Comput. Sci. 70(1), 102008 (2023).","journal-title":"J. Comput. Sci."},{"issue":"1","key":"10689_CR39","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.infoh.2024.12.002","volume":"2","author":"S Afolabi","year":"2025","unstructured":"Afolabi, S., Ajadi, N., Jimoh, A. & Adenekan, I. Predicting diabetes using supervised machine learning algorithms on E-health records. Inform. Health 2(1), 9\u201316 (2025).","journal-title":"Inform. Health"},{"issue":"20","key":"10689_CR40","first-page":"1","volume":"310","author":"J Amann","year":"2020","unstructured":"Amann, J. et al. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med. Inform. Decis. Mak. 310(20), 1\u20139 (2020).","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"62","key":"10689_CR41","first-page":"52","volume":"186","author":"K Muneer","year":"2025","unstructured":"Muneer, K. & Fatima, U. Cryptocurrencies analytics with machine learning and human-centered explainable AI: Enhancing decision-making in dynamic market. Int. J. Comput. Appl. 186(62), 52\u201367 (2025).","journal-title":"Int. J. Comput. Appl."},{"key":"10689_CR42","unstructured":"H1N1 and Seasonal Flu Vaccines (Kaggle, 2021)."},{"key":"10689_CR43","doi-asserted-by":"crossref","unstructured":"Kumar, G. et al. Flu shot learning: overview of H1N1 and seasonal flu vaccines. In 024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0, Raigarh, India (2024).","DOI":"10.1109\/OTCON60325.2024.10688147"},{"key":"10689_CR44","doi-asserted-by":"crossref","unstructured":"Adi, S. P., Bharadwaj, K. V. & Bettadapura Adishesha, V. Predicting seasonal vaccines and H1N1 vaccines using machine learning techniques. In Communications in Computer and Information Science, (Springer, 2021).","DOI":"10.1007\/978-3-030-88244-0_1"},{"issue":"3","key":"10689_CR45","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1016\/j.idc.2012.05.005","volume":"26","author":"JA Al-Tawfiq","year":"2012","unstructured":"Al-Tawfiq, J. A. & Memish, Z. A. Mass gatherings and infectious diseases: Prevention, detection, and control. Infect. Dis. Clin. N. Am. 26(3), 725\u2013737 (2012).","journal-title":"Infect. Dis. Clin. N. Am."},{"key":"10689_CR46","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/s40121-019-00262-9","volume":"8","author":"AR Muttalif","year":"2019","unstructured":"Muttalif, A. R. et al. Incidence and prevention of invasive meningococcal disease in global mass gathering events. Infect. Dis. Ther. 8, 569\u2013579 (2019).","journal-title":"Infect. Dis. Ther."},{"key":"10689_CR47","doi-asserted-by":"crossref","unstructured":"Hoang, V. T. & Gautret, P. Infectious diseases and mass gatherings. Curr. Infect. Dis. Rep. 20 (2018).","DOI":"10.1007\/s11908-018-0650-9"},{"key":"10689_CR48","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/s10157-018-1641-8","volume":"23","author":"J Ishigami","year":"2019","unstructured":"Ishigami, J. & Matsushita, K. Clinical epidemiology of infectious disease among patients with chronic kidney disease. Clin. Exp. Nephrol. 23, 437\u2013447 (2019).","journal-title":"Clin. Exp. Nephrol."},{"key":"10689_CR49","doi-asserted-by":"publisher","first-page":"S29","DOI":"10.1016\/j.ijid.2019.02.018","volume":"8","author":"C Ugarte-Gil","year":"2019","unstructured":"Ugarte-Gil, C., Carrillo-Larco, R. M. & Kirwan, D. E. Latent tuberculosis infection and non-infectious co-morbidities: Diabetes mellitus type 2, chronic kidney disease and rheumatoid arthritis. Int. J. Infect. Dis. 8, S29\u2013S31 (2019).","journal-title":"Int. J. Infect. Dis."},{"issue":"3","key":"10689_CR50","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.shaw.2020.06.001","volume":"11","author":"M Mhango","year":"2020","unstructured":"Mhango, M., Dzobo, M., Chitungo, I. & Dzinamarira, T. COVID-19 risk factors among health workers: A rapid review. Saf. Health Work. 11(3), 262\u2013265 (2020).","journal-title":"Saf. Health Work."},{"key":"10689_CR51","first-page":"589545","volume":"21","author":"FM Sirois","year":"2020","unstructured":"Sirois, F. M. & Owens, J. Factors associated with psychological distress in health-care workers during an infectious disease outbreak: A rapid systematic review of the evidence. Front. Psychiatry 21, 589545 (2020).","journal-title":"Front. Psychiatry"},{"key":"10689_CR52","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1007\/s10096-014-2119-6","volume":"33","author":"R Huttunen","year":"2014","unstructured":"Huttunen, R. & Syrj\u00e4nen, J. Healthcare workers as vectors of infectious diseases. Eur. J. Clin. Microbiol. Infect. Dis. 33, 1477\u20131488 (2014).","journal-title":"Eur. J. Clin. Microbiol. Infect. Dis."},{"issue":"2","key":"10689_CR53","doi-asserted-by":"publisher","first-page":"329","DOI":"10.3390\/pathogens12020329","volume":"12","author":"ZA Abdulla","year":"2023","unstructured":"Abdulla, Z. A. et al. the role of immunity in the pathogenesis of SARS-CoV-2 infection and in the protection generated by COVID-19 vaccines in different age groups. Pathogens 12(2), 329 (2023).","journal-title":"Pathogens"},{"key":"10689_CR54","doi-asserted-by":"crossref","unstructured":"Keener, A. Tailoring vaccines for older people and the very young. (2019).","DOI":"10.1038\/d41586-019-03638-6"},{"key":"10689_CR55","doi-asserted-by":"publisher","first-page":"1269","DOI":"10.1007\/s10115-019-01335-4","volume":"61","author":"A Tharwat","year":"2019","unstructured":"Tharwat, A. Parameter investigation of support vector machine classifier with kernel functions. Knowl. Inf. Syst. 61, 1269\u20131302 (2019).","journal-title":"Knowl. Inf. Syst."},{"key":"10689_CR56","unstructured":"XGBoost \u2014 How does this work. https:\/\/medium.com\/@prathameshsonawane\/xgboost-how-does-this-work-e1cae7c5b6cb."},{"key":"10689_CR57","unstructured":"Understanding Feed Forward Neural Networks With Maths and Statistics. https:\/\/www.turing.com\/kb\/mathematical-formulation-of-feed-forward-neural-network (Accessed Nov 2024)."},{"key":"10689_CR58","unstructured":"Feedforward neural network. https:\/\/www.geeksforgeeks.org\/feedforward-neural-network\/ (2024)."},{"key":"10689_CR59","unstructured":"Rectified Linear Unit (ReLU). https:\/\/www.deepchecks.com\/glossary\/rectified-linear-unit-relu\/. (Accessed Nov 2024)."},{"issue":"1","key":"10689_CR60","doi-asserted-by":"publisher","first-page":"308","DOI":"10.30574\/wjaets.2023.8.1.0054","volume":"8","author":"JC Obi","year":"2023","unstructured":"Obi, J. C. A comparative study of several classification metrics and their performances on data. World J. Adv. Eng. Technol. Sci. 8(1), 308\u2013314 (2023).","journal-title":"World J. Adv. Eng. Technol. Sci."},{"issue":"12","key":"10689_CR61","doi-asserted-by":"publisher","first-page":"3345","DOI":"10.1109\/TFUZZ.2019.2950636","volume":"28","author":"BF Mohit Kumar","year":"2019","unstructured":"Mohit Kumar, B. F. Fuzzy membership functional analysis for nonparametric deep models of image features. IEEE Trans. Fuzzy Syst. 28(12), 3345\u20133359 (2019).","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10689_CR62","doi-asserted-by":"publisher","first-page":"586408","DOI":"10.3389\/fpsyg.2020.586408","volume":"11","author":"SL Kexin Wang","year":"2020","unstructured":"Kexin Wang, S. L. Who is willing to engage in social gatherings during the coronavirus disease 2019 lockdown? A curvilinear relationship between age and heuristic processing. Front. Psychol. 11, 586408 (2020).","journal-title":"Front. Psychol."},{"key":"10689_CR63","unstructured":"COVID-19-geographic-disbtribution-worldwide-2020-05-10, Mendeley."},{"key":"10689_CR64","unstructured":"Health, Federal, Ministry Of Social Affairs (2020)."},{"key":"10689_CR65","unstructured":"D. B. V. M. I. K. Jonathan (2020)."},{"key":"10689_CR66","unstructured":"May 2020 Covid-19 Twitter Streaming Dataset (2021)."},{"issue":"10","key":"10689_CR67","doi-asserted-by":"publisher","first-page":"5350","DOI":"10.3390\/ijerph18105350","volume":"18","author":"MN Zahid","year":"2021","unstructured":"Zahid, M. N. & Perna, S. Continent-wide analysis of COVID 19: Total cases, deaths, tests, socio-economic, and morbidity factors associated to the mortality rate, and forecasting analysis in 2020\u20132021. Int. J. Environ. Res. Public Health. 18(10), 5350 (2021).","journal-title":"Int. J. Environ. Res. Public Health."},{"key":"10689_CR68","unstructured":"Kaggle (2021)."},{"key":"10689_CR69","unstructured":"Kaggle."},{"issue":"Part B","key":"10689_CR70","doi-asserted-by":"publisher","first-page":"110315","DOI":"10.1016\/j.compbiomed.2025.110315","volume":"192","author":"R Khushal","year":"2025","unstructured":"Khushal, R. & Fatima, U. Fuzzy quantum machine learning (FQML) logic for optimized disease prediction. Comput. Biol. Med. 192(Part B), 110315. https:\/\/doi.org\/10.1016\/j.compbiomed.2025.110315 (2025).","journal-title":"Comput. Biol. Med."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-10689-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-10689-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-10689-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T22:02:41Z","timestamp":1760997761000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-10689-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,16]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["10689"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-10689-5","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,16]]},"assertion":[{"value":"22 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"36139"}}