In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our framework show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about 3.03% for precision, 1.56% for recall, 3.17% for F1, and 1.56% for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place.

Department of Computer Science and Engineering New Technologies for Information Society University of West Bohemia Technicka 18 330 01 Pilsen Czech Republic

Department of Computer Science and Engineering University of West Bohemia Technicka 18 330 01 Pilsen Czech Republic

Diabetology Center 1st Department of Internal Medicine University Hospital Pilsen Alej Svobody 923 80 323 00 Pilsen Czech Republic

ICAR National Research Council of Italy Via P Castellino 80131 Naples Italy

Natural Computation Lab DIEM University of Salerno Via Giovanni Paolo 2 132 84084 Fisciano Italy

Zobrazit více v PubMed

Mitchell T.M. Machine Learning. Volume 1 McGraw-Hill; New York, NY, USA: 1997.

Liu B., Ding M., Shaham S., Rahayu W., Farokhi F., Lin Z. When machine learning meets privacy: A survey and outlook. ACM Comput. Surv. (CSUR) 2021;54:1–36. doi: 10.1145/3436755. DOI

Konečnỳ J., McMahan B., Ramage D. Federated optimization: Distributed optimization beyond the datacenter. arXiv. 20151511.03575

Konečnỳ J., McMahan H.B., Ramage D., Richtárik P. Federated optimization: Distributed machine learning for on-device intelligence. arXiv. 20161610.02527

Li Q., Wen Z., Wu Z., Hu S., Wang N., Li Y., Liu X., He B. A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Trans. Knowl. Data Eng. 2023;35:3347–3366. doi: 10.1109/TKDE.2021.3124599. DOI

McMahan H.B., Moore E., Ramage D., Agüera y Arcas B. Federated learning of deep networks using model averaging. arXiv. 20161602.05629

Yang Q., Liu Y., Chen T., Tong Y. Federated machine learning: Concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 2019;10:1–19. doi: 10.1145/3298981. DOI

Xu J., Jin Y., Du W., Gu S. A federated data-driven evolutionary algorithm. Knowl.-Based Syst. 2021;233:107532. doi: 10.1016/j.knosys.2021.107532. DOI

Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 2019;1:206–215. doi: 10.1038/s42256-019-0048-x. PubMed DOI PMC

Bengio Y. Learning deep architectures for AI. Found. Trends® Mach. Learn. 2009;2:1–127. doi: 10.1561/2200000006. DOI

Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw. 2015;61:85–117. doi: 10.1016/j.neunet.2014.09.003. PubMed DOI

Gunning D. Explainable artificial intelligence (xai) Def. Adv. Res. Proj. Agency (DARPA) Nd Web. 2017;2:1. doi: 10.1126/scirobotics.aay7120. PubMed DOI

Montavon G., Samek W., Müller K.R. Methods for interpreting and understanding deep neural networks. Digit. Signal Process. 2018;73:1–15. doi: 10.1016/j.dsp.2017.10.011. DOI

Back T. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press; Oxford, UK: 1996.

Back T., Fogel D.B., Michalewicz Z. Handbook of Evolutionary Computation. IOP Publishing Ltd.; Bristol, UK: 1997.

Whitley D. An overview of evolutionary algorithms: Practical issues and common pitfalls. Inf. Softw. Technol. 2001;43:817–831. doi: 10.1016/S0950-5849(01)00188-4. DOI

Tomassini M. Evolutionary Algorithms in Engineering and Computer Science. John Wiley & Sons; Chichester, UK: 1999. Parallel and distributed evolutionary algorithms: A review; pp. 113–133.

Gong Y.J., Chen W.N., Zhan Z.H., Zhang J., Li Y., Zhang Q., Li J.J. Distributed evolutionary algorithms and their models: A survey of the state-of-the-art. Appl. Soft Comput. 2015;34:286–300. doi: 10.1016/j.asoc.2015.04.061. DOI

Ryan C., Collins J.J., O’Neill M. Grammatical evolution: Evolving programs for an arbitrary language; Proceedings of the European Conference on Genetic Programming; Paris, France. 14–15 April 1998; pp. 83–96.

O’Neill M., Ryan C. Grammatical evolution. IEEE Trans. Evol. Comput. 2001;5:349–358. doi: 10.1109/4235.942529. DOI

Blanco-Justicia A., Domingo-Ferrer J., Martínez S., Sánchez D., Flanagan A., Eeik Tan K. Achieving security and privacy in federated learning systems: Survey, research challenges and future directions. Eng. Appl. Artif. Intell. 2021;106:104468. doi: 10.1016/j.engappai.2021.104468. DOI

Mothukuri V., Parizi R.M., Pouriyeh S., Huang Y., Dehghantanha A., Srivastava G. A survey on security and privacy of federated learning. Future Gener. Comput. Syst. 2021;115:619–640. doi: 10.1016/j.future.2020.10.007. DOI

Li L., Fan Y., Tse M., Lin K.Y. A review of applications in federated learning. Comput. Ind. Eng. 2020;149:106854. doi: 10.1016/j.cie.2020.106854. DOI

Banabilah S., Aloqaily M., Alsayed E., Malik N., Jararweh Y. Federated learning review: Fundamentals, enabling technologies, and future applications. Inf. Process. Manag. 2022;59:103061. doi: 10.1016/j.ipm.2022.103061. DOI

Regulation P. General data protection regulation. Intouch. 2018;25:1–5.

Sun H., Saeedi P., Karuranga S., Pinkepank M., Ogurtsova K., Duncan B.B., Stein C., Basit A., Chan J.C., Mbanya J.C., et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res. Clin. Pract. 2022;183:109119. doi: 10.1016/j.diabres.2021.109119. PubMed DOI PMC

Papatheodorou K., Banach M., Bekiari E., Rizzo M., Edmonds M. Complications of diabetes 2017. J. Diabetes Res. 2018;2018:3086167. doi: 10.1155/2018/3086167. PubMed DOI PMC

Marling C., Bunescu R. The OhioT1DM dataset for blood glucose level prediction: Update 2020. NIH Public Access. 2020;2675:71. PubMed PMC

Woldaregay A., Arsand E., Botsis T., Albers D., Mamykina L., Hartvigsen G. Data-driven blood glucose pattern classification and anomalies detection: Machine-learning applications in type 1 diabetes. J. Med. Internet Res. 2019;21:e11030. doi: 10.2196/11030. PubMed DOI PMC

Battelino T., Danne T., Bergenstal R.M., Amiel S.A., Beck R., Biester T., Bosi E., Buckingham B.A., Cefalu W.T., Close K.L., et al. Clinical targets for continuous glucose monitoring data interpretation: Recommendations from the international consensus on time in range. Diabetes Care. 2019;42:1593–1603. doi: 10.2337/dci19-0028. PubMed DOI PMC

Abdoli S., Hessler D., Smither B., Miller-Bains K., Burr E.M., Stuckey H.L. New insights into diabetes burnout and its distinction from diabetes distress and depressive symptoms: A qualitative study. Diabetes Res. Clin. Pract. 2020;169:108446. doi: 10.1016/j.diabres.2020.108446. PubMed DOI

Rodríguez-Barroso N., Jiménez-López D., Luzón M.V., Herrera F., Martínez-Cámara E. Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges. Inf. Fusion. 2023;90:148–173. doi: 10.1016/j.inffus.2022.09.011. DOI

Rudin C., Chen C., Chen Z., Huang H., Semenova L., Zhong C. Interpretable machine learning: Fundamental principles and 10 grand challenges. Stat. Surv. 2022;16:1–85. doi: 10.1214/21-SS133. DOI

Mengnan D., Ninghao L., Xia H. Techniques for interpretable machine learning. Commun. ACM. 2020;63:68–77.

De Falco I., Della Cioppa A., Koutny T., Krcma M., Scafuri U., Tarantino E. Genetic programming-based induction of a glucose-dynamics model for telemedicine. J. Netw. Comput. Appl. 2018;119:1–13. doi: 10.1016/j.jnca.2018.06.007. DOI

De Falco I., Della Cioppa A., Giugliano A., Marcelli A., Koutny T., Krcma M., Scafuri U., Tarantino E. A genetic programming-based regression for extrapolating a blood glucose-dynamics model from interstitial glucose measurements and their first derivatives. Appl. Soft Comput. 2019;77:316–328. doi: 10.1016/j.asoc.2019.01.020. DOI

Tyler N.S., Jacobs P.G. Artificial intelligence in decision support systems for type 1 diabetes. Sensors. 2020;20:3214. doi: 10.3390/s20113214. PubMed DOI PMC

Felizardo V., Garcia N.M., Pombo N., Megdiche I. Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction—A systematic literature review. Artif. Intell. Med. 2021;118:1–15. doi: 10.1016/j.artmed.2021.102120. PubMed DOI

Della Cioppa A., De Falco I., Koutny T., Ubl M., Scafuri U., Tarantino E. Reducing high-risk glucose forecasting errors by evolving interpretable models for type 1 diabetes. Appl. Soft Comput. 2023;134:110012. doi: 10.1016/j.asoc.2023.110012. DOI

Maniruzzaman M., Rahman M., Ahammed B. Classification and prediction of diabetes disease using machine learning paradigm. Health Inf. Sci. Syst. 2020;8:1–14. doi: 10.1007/s13755-019-0095-z. PubMed DOI PMC

Sharma T., Shah M. A comprehensive review of machine learning techniques on diabetes detection. Vis. Comput. Ind. Biomed. Art. 2021;4:30. doi: 10.1186/s42492-021-00097-7. PubMed DOI PMC

Rastogi R., Bansal M. Diabetes prediction model using data mining techniques. Meas. Sensors. 2023;25:100605. doi: 10.1016/j.measen.2022.100605. DOI

Cuesta-Frau D., Miró–Martínez P., Oltra–Crespo S., Jordán–Núñez J., Vargas B., Vigil L. Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm. Comput. Methods Programs Biomed. 2018;165:197–204. doi: 10.1016/j.cmpb.2018.08.018. PubMed DOI

Lekha S., Suchetha M. Real-time non-invasive detection and classification of diabetes using modified convolution neural network. IEEE J. Biomed. Health Inform. 2018;22:1630–1636. doi: 10.1109/JBHI.2017.2757510. PubMed DOI

Wan S., Liang Y., Zhang Y. Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput. Electr. Eng. 2018;72:274–282. doi: 10.1016/j.compeleceng.2018.07.042. DOI

Kannadasan K., Damodar Reddy E. andVenkatanareshbabu, K. Type 2 diabetes data classification using stacked autoencoders in deep neural networks. Clin. Epidemiol. Glob. Health. 2019;7:530–535. doi: 10.1016/j.cegh.2018.12.004. DOI

Zhu T., Li K., Herrero P., Chen J., Georgiou P. A Deep Learning Algorithm for Personalized Blood Glucose Prediction; Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data (KDH), within the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018), CEUR Workshop Proceedings; Stockholm, Schweden. 8–12 July 2018; pp. 64–78.

Bevan R., Coenen F. Experiments in non-personalized future blood glucose level prediction; Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data (KDH), within the 24th European Conference on Artificial Intelligence (ECAI2020), CEUR Workshop Proceedings; Santiago de Compostela, Spain. 29–30 August 2020; pp. 100–104.

Mayo M., Koutny T. Neural multi-class classification approach to blood glucose level forecasting with prediction uncertainty visualisation; Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data (KDH), within the 24th European Conference on Artificial Intelligence (ECAI2020), CEUR Workshop Proceedings; Santiago de Compostela, Spain. 29–30 August 2020; pp. 80–84.

Liu Y., Liu W., Chen H., Cai X., Zhang R., An Z., Shi D., Ji L. Graph convolutional network enabled two-stream learning architecture for diabetes classification based on flash glucose monitoring data. Biomed. Signal Process. Control. 2021;69:102896. doi: 10.1016/j.bspc.2021.102896. DOI

Yin H., Mukadam B., Dai X., Jha N.K. DiabDeep: Pervasive diabetes diagnosis based on wearable medical sensors and efficient neural networks. IEEE Trans. Emerg. Top. Comput. 2021;9:1139–1150. doi: 10.1109/TETC.2019.2958946. DOI

Kumari S., Kumar D., Mittal M. An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. Int. J. Cogn. Comput. Eng. 2021;2:40–46. doi: 10.1016/j.ijcce.2021.01.001. DOI

Ganie S., Malik M. An ensemble machine learning approach for predicting type-II diabetes mellitus based on lifestyle indicators. Healthc. Anal. 2022;2:100092. doi: 10.1016/j.health.2022.100092. DOI

Gupta H., Varshney H., Sharma T.K., Pachauri N., Verma O.P. Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction. Complex Intell. Syst. 2022;8:3073–3087. doi: 10.1007/s40747-021-00398-7. DOI

Joseph L.P., Joseph E.A., Ramendra P. Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture. Comput. Biol. Med. 2022;151:106178. doi: 10.1016/j.compbiomed.2022.106178. PubMed DOI

Lundberg S.M., Nair B., Vavilala M.S., Horibe M., Eisses M.J., Adams T., Liston D.E., Low D.K.W., Newman S.F., Kim J., et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. 2018;2:749–760. doi: 10.1038/s41551-018-0304-0. PubMed DOI PMC

Sisodia D., Sisodia D.S. Prediction of diabetes using classification algorithms. Procedia Comput. Sci. 2018;132:1578–1585. doi: 10.1016/j.procs.2018.05.122. DOI

Cuesta H.A., Coffman D.L., Branas C., Murphy H.M. Using decision trees to understand the influence of individual- and neighborhood-level factors on urban diabetes and asthma. Health Place. 2019;58:102119. doi: 10.1016/j.healthplace.2019.04.009. PubMed DOI

Sneha C., Gangil T. Analysis of diabetes mellitus for early prediction using optimal features selection. J. Big Data. 2019;6:13. doi: 10.1186/s40537-019-0175-6. DOI

Hasan K., Alam A., Das D., Hossain E., Hasan M. Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access. 2020;8:76516–76531. doi: 10.1109/ACCESS.2020.2989857. DOI

Choubey D.K., Kumar P., Tripathi S., Kumar S. Performance evaluation of classification methods with PCA and PSO for diabetes. Netw. Model. Anal. Health Inform. Bioinform. 2020;9:1–30. doi: 10.1007/s13721-019-0210-8. DOI

Özmen E.P., Özcan T. Diagnosis of diabetes mellitus using artificial neural network and classification and regression tree optimized with genetic algorithm. J. Forecast. 2020;39:661–670. doi: 10.1002/for.2652. DOI

Naveen Kishore G., Rajesh V., Vamsi Akki Reddy A., Sumedh K., Rajesh Sai Reddy T. Prediction of diabetes using machine learning classification algorithms. Int. J. Sci. Technol. Res. 2020;1:1805–1808.

Torkey H., Ibrahim E., El-Din Hemdan E., El-Sayed A., Shouman M.A. Diabetes classification application with efficient missing and outliers data handling algorithms. Complex Intell. Syst. 2022;8:237–253. doi: 10.1007/s40747-021-00349-2. DOI

Hassan M.M., Mollick S., Yasmin F. An unsupervised cluster-based feature grouping model for early diabetes detection. Healthc. Anal. 2022;2:10o112.

Ahmedi U., Issa G.F., Khan M.A., Aftab S., Khang M.F., Said R.A.T., Ghazal T.M., Ahmad M. Prediction of diabetes empowered with fused machine learning. IEEE Access. 2022;10:8529–8538. doi: 10.1109/ACCESS.2022.3142097. DOI

Chang V., Ganatra M.A., Hall K., Golightly L., Xu Q.A. An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators. Healthc. Anal. 2022;2:100118. doi: 10.1016/j.health.2022.100118. DOI

Thotad P.N., Bharamagoudar G.R., Anami B.S. Diabetes disease detection and classification on Indian demographic and health survey data using machine learning methods. Diabetes Metab. Syndr. Clin. Res. Rev. 2023;17:102690. doi: 10.1016/j.dsx.2022.102690. PubMed DOI

Theerthagiri P., Ruby A.U., Vidya J. Diagnosis and classification of the diabetes using machine learning algorithms. SN Comput. Sci. 2023;4:1–10. doi: 10.1007/s42979-022-01485-3. DOI

Fürnkranz J. Encyclopedia of Machine Learning. Springer; Boston, MA, USA: 2010. Decision Tree; pp. 263–267.

Alvarado J., Velasco J.M., Hidalgo J.I., Fernández de Vega F. Blood Glucose Prediction Using a Two Phase TSK Fuzzy Rule Based System; Proceedings of the IEEE Congress on Evolutionary Computation (CEC); Kraków, Poland. 28 June–1 July 2021; pp. 1–8.

De La Cruz M., Cervigón C., Alvarado J., Botella-Serrano M., Hidalgo J.I. Evolving Classification Rules for Predicting Hypoglycemia Events; Proceedings of the IEEE Congress on Evolutionary Computation (CEC); Padua, Italy. 18–23 July 2022; pp. 1–8.

Sun C., Ippel L., Dekker A., Dumontier M., van Soes J. A systematic review on privacy-preserving distributed data mining. Data Sci. 2021;4:121–150. doi: 10.3233/DS-210036. DOI

Xu J., Glicksberg B.S., Su C., Walker P., Bian J., Wang F. Federated learning for healthcare informatics. J. Healthc. Inform. Res. 2021;5:1–19. doi: 10.1007/s41666-020-00082-4. PubMed DOI PMC

Rahman A., Hossain M.S., Muhammad G., Kundu D., Debnath T., Rahman M., Khan M.S.I., Tiwari P., Band S.S. Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues. Cluster Comput. 2022:1–41. doi: 10.1007/s10586-022-03658-4. PubMed DOI PMC

Wang W., Li X., Qiu X., Zhang X., Brusic V., Zhao J. A privacy preserving framework for federated learning in smart healthcare systems. Inf. Process. Manag. 2023;60:103167. doi: 10.1016/j.ipm.2022.103167. DOI

Lai J., Song X., Wang R., Li X. Edge intelligent collaborative privacy protection solution for smart medical. Cyber Secur. Appl. 2023;1:100010. doi: 10.1016/j.csa.2022.100010. DOI

Liu J., Xi L., Yang H., Zhuang L. A diabetes prediction system based on federated learning; Proceedings of the IEEE International Conference on Big Data, Information and Computer Network (BDICN); Sanya, China. 20–22 January 2022; pp. 486–491.

Islam H., Mosa A., Famia A federated mining approach on predicting diabetes-related complications: Demonstration using real-world clinical data; Proceedings of the AMIA Annual Symposium Proceedings; Washington, DC, USA. 5–9 November 2022; pp. 556–564. PubMed PMC

Dubreuil M., Gagn e C., Parizeau M. Analysis of a master-slave architecture for distributed evolutionary computations. IEEE Trans. Syst. Man Cybern.-Part B. 2001;36:229–235. doi: 10.1109/TSMCB.2005.856724. PubMed DOI

Cantú-Paz E. Migration policies, selection pressure, and parallel evolutionary algorithms. J. Heuristics. 2001;7:311–334. doi: 10.1023/A:1011375326814. DOI

O’Neill M., Ryan C. Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers; New York, NY, USA: 2003.

Marling C., Bunescu R. The OhioT1DM dataset for blood glucose level prediction; Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data (KDH); Stockholm, Sweden. 8–12 July 2018; pp. 60–63.

Hovorka R., Canonico V., Chassin L.J., Haueter U., Massi-Benedetti M., Orsini Federici M., Pieber T.R., Schaller H.C., Schaupp L., Vering T., et al. Nonlinear model predictive control of glucose concentration in subjects with Type 1 diabetes. Physiol. Meas. 2004;25:905–920. doi: 10.1088/0967-3334/25/4/010. PubMed DOI

Hovorka R., Powrie J.K., Smith G.D., Sönksen P.H., Carson E.R., Jones R.H. Five-compartment model of insulin kinetics and its use to investigate action of chloroquine in NIDDM. Am. J. Physiol. 1993;265:162–1752. doi: 10.1152/ajpendo.1993.265.1.E162. PubMed DOI

Livesey G., Wilson P.D., Dainty J.R., Brown J.C., Faulks R.M., Roe M.A., Newman T.A., Eagles J., Mellon F.A., Greenwood R.H. Simultaneous time-varying systemic appearance of oral and hepatic glucose in adults monitored with stable isotopes. Am. J. Physiol. 1998;275:717–728. doi: 10.1152/ajpendo.1998.275.4.E717. PubMed DOI

Ni J., Drieberg R., Rockett P. The use of an analytic quotient operator in Genetic Programming. IEEE Trans. Evol. Comput. 2013;17:146–152. doi: 10.1109/TEVC.2012.2195319. DOI

Varela Lorenzo A., Delgado Gutierrez A. Ph.D. Thesis. University of Madrid; Madrid, Spain: 2020. Glucose Classification and Prediction System with Neural Networks.

Danne T., Nimri R., Battelino T., Bergenstal R.M., Close K.L., DeVries J.H., Garg S., Heinemann L., Hirsch I., Amiel S.A., et al. International consensus on use of continuous glucose monitoring. Diabetes Care. 2017;40:1631–1640. doi: 10.2337/dc17-1600. PubMed DOI PMC

Sun Y., Wong A.K., Kamel M. Classification of imbalanced data: A review. Int. J. Pattern Recognit. Artif. Intell. 2009;23:687–719. doi: 10.1142/S0218001409007326. DOI

Hidalgo J.I., Colmenar J.M., Kronberger G., Winkler S.M., Garnica O., Lanchares J. Data based prediction of blood glucose concentrations using evolutionary methods. J. Med. Syst. 2017;41:1–20. doi: 10.1007/s10916-017-0788-2. PubMed DOI

Contador S., Colmenar J.M., Garnica O., Velasco J.M., Hidalgo J.I. Blood glucose prediction using multi-objective grammatical evolution: Analysis of the “agnostic” and “what-if” scenarios. Genet. Program. Evolvable Mach. 2022;23:161–192. doi: 10.1007/s10710-021-09424-6. DOI

De Falco I., Della Cioppa A., Koutny T., Krcma M., Scafuri U., Tarantino E. A Grammatical Evolution approach for estimating blood glucose levels; Proceedings of the 11th IEEE Global Communications Conf.-Int. Workshop on AI-driven Smart Healthcare (AIdSH); Taipei, Taiwan. 7–11 December 2020; pp. 1–6.

Rodríguez-Fdez I., Canosa A., Mucientes M., Bugarín A. STAC: A web platform for the comparison of algorithms using statistical tests; Proceedings of the IEEE international conference on fuzzy systems (FUZZ-IEEE); Istanbul, Turkey. 2–5 August 2015; pp. 1–8.

Derrac J., García S., Molina D., Herrera F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 2011;1:3–18. doi: 10.1016/j.swevo.2011.02.002. DOI