Background/Objectives : The accurate prediction of Blood Glucose Levels (BGLs ) is important for the effective management of diabetes. Artificial Intelligence (AI) has become an effective tool for forecasting BGLs and offering decision support and therapeuti c interventions. In this study, existing AI techniques for predicting BGLs were reviewed systematically to identify their limitations and strengths. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta - Analyses ( PRISMA ) guidelin es, a systematic review of relevant studies was performed. Research was identified through a detailed search of several databases including PubMed, Institute of Electrical and Electronics Engineers (IEEE ) Explore, and Scholar with a focus on publications r anging from 2010 to 2025. Search terms included “AI for predicting glucose levels in diabetes ” and related keywords. Non -English and irrelevant studies were excluded. Results: Fifty -seven studies met the inclusion criteria. They used AI models such as Bidi rectional Long Short -Term Memory ( Bi-LSTM ) and Convolutional Neural Network ( CNN ), decision trees, and hybrid models like CNN -Gated Recurrent Unit ( GRU ). Many models demonstrated high accuracy, especially Bi-LSTM and CNN -GRU hybrids, but were often limited by small datasets, lack of generalizability, and low interpretability. Non - invasive sensing technologies and real -time systems using Internet of Things (IoT ) and wearable devices showed significant promise but require further validation. Conclusions: AI -based glucose prediction systems have advanced significantly, with some models achieving Root Mean Squared Error (RMSE ) values below 10 mg/dL in short -term forecasting. However, no single approach consistently outperforms others across all prediction horizons and patient scenarios. Future research should focus o n integrating multimodal inputs, enhancing model interpretability, and validating systems using diverse, real -world datasets to ensure clinical reliability.

[1] Ahmed A., Aziz S., Abd -Alrazaq A., Farooq F., and et al., “The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review ,” Journal of Medical Internet Research , vol. 25, pp. 1 -16, 2023. https://doi.org/10.2196/40259

[2] Alfian G., Syafrudin M., Anshari M., Benes F., and et al., “Blood Glucose Prediction Model for Type 1 Diabetes Based on Artificial Neural Network with Time -Domain Features ,” Biocybernetics an d Biomedical Engineering , vol. 40, no. 4, pp. 1586 -1599, 2020. https://doi.org/10.1016/j.bbe.2020.10.004

[3] Alhmiedat T and Alotaibi M., “The Investigation of Employing Supervised Machine Learning Models to Predict Type 2 Diabetes Among Adults ,” KSII Transact ions on Internet and Information Systems , vol. 16, no. 9, pp. 2904 -2926, 2022. DOI:10.3837/tiis.2022.09.004

[4] Alkanhel R., Saleh H., Elaraby A., Alharbi S., and et al., “Hybrid CNN -GRU Model for Real -Time Blood Glucose Forecasting: Enhancing IoT -Based Diabet es Management with AI ,” Sensors , vol. 24, no. 23, pp. 1 -16, 2024. https://doi.org/10.3390/s24237670

[5] Alzboon M., Al -Batah M., Alqaraleh M., Abuashour A., and Hamadah Bader A., “Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods ,” International Journal of Online and Biomedical Engineering , vol. 19, no. 15, pp. 144 -165, 2023. https://online - journals.org/index.php/i -joe/article/view/42417

[6] Amar Y., Shilo S., Oron T., Amar E., and et al., “Clinically Accurate Prediction of Glucose Level s in Patients with Type 1 Diabetes ,” Diabetes 1002 The International Arab Journal of Information Technology, Vol. 23, No. 5, September 2026 Technology and Therapeutics , vol. 22, no. 8, pp. 562 -569, 2020. DOI: 10.1089/dia.2019.0435

[7] Anjum M., Saher R., and Saeed M., “Optimizing Type 2 Diabetes Management: AI -Enhanced Time Series Analysis of Continuou s Glucose Monitoring Data for Personalized Dietary Intervention ,” PeerJ Computer Science , vol. 10, pp. 1 -28, 2024. DOI: 10.7717/peerj -cs.1971

[8] Annuzzi G., Apicella A., Arpaia P., Bozzetto L.,and et al., “Impact of Nutritional Factors in Blood Glucose Predic tion in Type 1 Diabetes Through Machine Learning ,” IEEE Access , vol. 11, pp. 17104 -17115, 2023. DOI: 10.1109/ACCESS.2023.3244712

[9] Annuzzi G., Bozzetto L., Cataldo A., Criscuolo S., and Pesola M., “Predicting and Monitoring Blood Glucose Through Nutritional Factors in Type 1 Diabetes by Artificial Neural Networks ,” Acta Imeko , vol. 12, no. 2, pp. 1 -7, 2023. DOI: https://doi.org/10.21014/actaimeko.v12i2.1453

[10] Aouragh A., Bahaj M., and Toufik F., “Diabetes Prediction: Optimization of Machine Learning through Fea ture Selection and Dimensionality Reduction ,” International Journal of Online and Biomedical Engineering , vol. 20, no. 8, pp. 100 - 114, 2024. DOI: https://doi.org/10.3991/ijoe.v20i08.47765

[11] Askari M., Rashid M., Sun X., Sevil M., and et al., “Meal and Physic al Activity Detection from Free - living Data for Discovering Disturbance Patterns to Glucose Levels in People with Diabetes ,” BioMedInformatics , vol. 2, no. 2, pp. 297 -317, 2022. DOI: 10.3390/biomedinformatics2020019

[12] Basile I and Sannino G., “Blood Glucose Level Prediction in Type 1 Diabetes: A Comparative Analysis of Interpretable Artificial Intelligence Approaches ,” Results in Engineering , vol. 25, no. 103681, pp. 1 -10, 2025. https://doi.org/10.1016/j.rineng.2024.103681

[13] Beck R., “Closing in on Closed -Loop Systems for Type 2 Diabetes ,” Nature Medicine , vol. 29, pp. 33 -34, 2023. https://doi.org/10.1038/s41591 -022 - 02127 -0

[14] Bertachi A., Biagi L., Contreras I., Luo N., and Vehi J., “Prediction of Blood Glucose Levels and Nocturnal Hypoglycemia Using Physiological Models and Artificial Neural Networks ,” in Proceedings of the 3 rd International Workshop on Knowledge Discovery in Healthcare Data, Stockholm , Stockholm, pp. 1 -6, 2018. https://api.semanticscholar.org/CorpusID:519515 68

[15] Bertachi A., Vinals C., Biagi L., Co ntreras I., and et al., “Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes Under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor ,” Sensors , vol. 20, no. 6, pp. 1 -11, 2020. DOI: 10.3390/s20061705

[16] Bunescu R., Struble N., Marling C., Shubrook J., and Schwartz F., “Blood Glucose Level Prediction Using Physiological Models and Support Vector Regression ,” in Proceedings of the IEEE 12 th International Conference on Machine Learning and Applications , Flo rida, Vol. 1, pp. 135 -140, 2013. DOI: 10.1109/ICMLA.2013.30

[17] Cameron F., Bequette B., Wilson D., Buckingham B., Lee H., and Niemeyer G., “A Closed -Loop Artificial Pancreas Based on Risk Management ,” Journal of Diabetes Science and Technology , vol. 5, no. 2, pp. 368 -379, 2011. DOI: 10.1177/193229681100500226

[18] Cloete L., “Diabetes Mellitus: An Overview of the Types, Symptoms, Complications and Management ,” Nurs Stand , vol. 37, no. 1, pp. 61 - 66, 2021. DOI: 10.7748/ns.2021.e11709

[19] Contreras I and Vehi J., “Artific ial Intelligence for Diabetes Management and Decision Support: Literature Review ,” Journal of Medical Internet Research , vol. 20, no. 5, pp. 1 -37, 2018. DOI: 10.2196/10775

[20] D’Antoni F., Merone M., Piemonte V., Iannello G., and Soda P., “Auto -Regressive Time Delayed Jump Neural Network for Blood Glucose Levels Forecasting ,” Knowledge -Based Systems , vol. 203, pp. 106134, 2020. https://doi.org/10.1016/ j.knosys. 2020.106134

[21] Daniels J., Herrero P., and Georgiou P., “A Multitask Learning Approach to Personalized Blood Glucose Prediction ,” IEEE Journal of Biomedical and Health Informatics , vol. 26, no. 1, pp. 436 -445, 2022. https://doi.org/10.1109/JBHI.2021.3100558

[22] Dasilva J., “Diabetes,” https://www.kaggle.com/johndasilva/diabetes Last Visited, 2025.

[23] Dave D., DeSa lvo D., Haridas B., McKay S., and et al., “Feature -Based Machine Learning Model for Real -Time Hypoglycemia Prediction ,” Journal of Diabetes Science and Technology , vol. 15, no. 4, pp. 8420 -855, 2021. DOI: 10.1177/1932296820922622

[24] Del Favero, S., Facchinett i A., and Cobelli C., “A Glucose -Specific Metric to Assess Predictors and Identify Models ,” IEEE Transactions on Biomedical Engineering , vol. 59, no. 5, pp. 1281 - 1290, 2012. DOI: 10.1109/TBME.2012.2185234

[25] Dubosson F., Ranvier J., Bromuri S., Calbimonte J., and et al., “The Open D1NAMO Dataset: A Multi -Modal Dataset for Research on Non - Invasive Type 1 Diabetes Management ,” Informatics in Medicine Unlocked , vol. 13, pp. 92 - 100, 2018. https://doi.org/10.1016/j.imu.2018.09.003

[26] Ekeland A., Bowes A., and Flottorp S., “Methodologies for Assessing Telemedicine: A Systematic Review of Reviews ,” International Journal of Medical Informatics , vol. 81, no. 1, pp. 1-11, 2012. DOI: 10.1016/j.ijmedinf.2011.10.009

[27] Elhadd T., Mall R., Bashir M., Palotti J., and et al., “Artif icial Intelligence (AI) Based Machine Learning Models Predict Glucose Variability and Hypoglycaemia Risk in Patients with Type 2 Diabetes on a Multiple Drug Regimen Who Fast During Ramadan (The PROFAST -IT Ramadan Study) ,” Diabetes Research and Clinical Pra ctice , vol. 169, pp. 1 -9, 2020. DOI: 10.1016/j.diabres.2020.108388

[28] Ellahham S., “Artificial Intelligence: The Future for Diabetes Care ,” American Journal of Medicine , vol. 133, no. 8, pp. 895 -900, 2020. DOI: 10.1016/j.amjmed.2020.03.033

[29] Eskaf K., “Advanced AI Techniques for Real - Time Blood Glucose Prediction in Diabetics: A Study Using Deep Learning and Genetic Algorithms ,” Midocean Journal for Research and Scientific Studies , vol. 1, no. 1, pp. 123 -135, 2024. DOI: https://doi.org/10.71335/5y36na30

[30] Estremera E., Cabrera A., Beneyto A., and Vehi J., “A simulator with realistic and Challenging Scenarios for Virtual T1D Patients Undergoing CSII and MDI Therapy ,” Journal of Biomedical Informatics , vol. 132, art no. 104141, pp. 1 -13, 2022. DOI: 10.1016/j. jbi.2022.104141

[31] Faccioli S., Prendin F., Facchinetti A., Sparacino G., and Del Favero S., “Combined Use of Glucose -Specific Model Identification and Alarm Strategy Based On Prediction -Funnel to Improve Online Forecasting of Hypoglycemic Events ,” Journal of Diabetes Science and Technology , vol. 17, no. 5, pp. 1295 -1303, 2023. DOI: 10.1177/19322968221093665

[32] Floris L., “Type 1 Diabetes Blood Glucose Prediction,” Kaggle , https://www.kaggle.com/datasets/lacofloris/type - 1-diabetes -blood -glucose -prediction, Last V isited, 2025.

[33] Georga E., Protopappas V., Ardigo D., Marina M., and et al., “Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression ,” IEEE Journal of Biomedical and Health Informatics , vo l. 17, no. 1, pp. 71 -81, 2013. DOI: 10.1109/TITB.2012.2219876

[34] Georga E., Protopappas V., Polyzos D., and Fotiadis D., “A Predictive Model of Subcutaneous Glucose Concentration in Type 1 Diabetes Based on Random Forests ,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society , California, 2889 -2892, 2012. DOI: 10.1109/EMBC.2012.6346565

[35] Guemes A., Cappon G., Hernandez B., Reddy M., and et al., “Predicting Quality of Overnight Glycaemic Control in Ty pe 1 Diabetes Using Binary Classifiers ,” IEEE Journal of Biomedical and Health Informatics , vol. 24, no. 5, pp. 1439 - 1446, 2020. DOI: 10.1109/JBHI.2019.2938305

[36] Haidar A., Wilinska M., Graveston J., and Hovorka R., “Stochastic Virtual Population of Subjects with Type 1 Diabetes for The Assessment of Closed -Loop Glucose Controllers ,” IEEE Transactions on Biomedical Engineering , vol. 60, no. 12, pp. 3524 -3533, 2013. DOI: 10.1109/TBME.2013.2272736

[37] Hameed E., Joshi H., and Kadhim Q., “Advancements in Artificial Intelligence Techniques for Diabetes Prediction: A Comprehensive Literature Review ,” Journal of Robotics and Control , vol. 6, no. 1, pp. 345 -365, 2025. https://doi.org/10.18196/jrc.v6i1.22258

[38] Heinemann L. and Freckmann G., “CGM Versus FGM; or, Continuous G lucose Monitoring is Not Flash Glucose Monitoring ,” Journal of Diabetes Science and Technology , vol. 9, no. 5, pp. 947 -950, 2015. DOI: 10.1177/1932296815603528

[39] Institute for Health Metrics and Evaluation, Global Diabetes Cases To Soar From 529 Million to 1.3 Billion by 2050, https:/ /www.healthdata.org/news - events/newsroom/news -releases/global -diabetes - cases -soar -529 -million -13 -billion -2050 , Last Visited, 2025

[40] International Diabetes Federation, About Diabetes, https://worlddiabet esday.org/about - wdd/ , Last Visited,2025

[41] Jacobs P., Herrero P., Facchinetti A., Vehi J., and et al., “Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities ,” IEEE Reviews in Biom edical Engineering , vol. 17, pp. 19 -41, 2024. DOI: 10.1109/RBME.2023.3331297

[42] Jaeb Center for Health Research, “Listing of Available T1D Data Sets,” https://public.jaeb.org/datasets/diabetes, Last Visited, 2025.

[43] Jaeb Center for Health Research, DirecNet Dat aset, “Evaluation of Counter -Regulatory Hormone Responses during Hypoglycemia and the Accuracy of Continuous Glucose Monitors in Children with T1DM,” ClinicalTrials.gov, NCT00607139, https://clinicaltrials.gov/study/NCT00607139, Last Visited, 2025.

[44] Jahangi r Z., Shah Y., Qureshi S., Qureshi H., and etal., “From Data to Decisions: The AI Revolution in Diabetes Care ,” International Journal of Membrane Science and Technology , vol. 10, no. 5, pp. 1162 -1179, 2023. https://www.researchgate.net/publication/383359 86 5_From_Data_to_Decisions_The_AI_Revoluti on_in_Diabetes_Care 1004 The International Arab Journal of Information Technology, Vol. 23, No. 5, September 2026

[45] Jensen M., Dethlefsen C., Vestergaard P., and Hejlesen, O., “Prediction of Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data in People with Type 1 Diabetes: A Proof -of-Concept Study ,” Journal of Diabetes Science and Technology , vol. 14, no. 2, pp. 250 - 256, 2020. DOI: 10.1177/1932296819868727

[46] Keuning W., Medtronic MiniMed 640G and MiniMed 670G, https://github.com/ictinnovaties - zorg/dataset -diabetes -adolescents -time -series - with -heart -rat e, GitHub, Last Visited, 2025.

[47] Kushner T., Breton M., and Sankaranarayanan S., “Multi -Hour Blood Glucose Prediction in Type 1 Diabetes: A Patient -Specific Approach Using Shallow Neural Network Models ,” Diabetes Technology and Therapeutics , vol. 22, no. 12, pp. 883 -891, 2020. DOI: 10.1089/dia.2020.0061

[48] Li K., Daniel J., Liu C., Herrero P., and Georgiou P., “Convolutional recurrent neural networks for glucose prediction ,” IEEE Journal of Biomedical and Health Informatics , vol. 24, no. 2, pp. 603 -613, 2020. DO I: 10.1109/JBHI.2019.2908488

[49] Li K., Liu C., Zhu T., Herrero P., and Georgiou P., “GluNet: A Deep Learning Framework for Accurate Glucose Forecasting ,” IEEE Journal of Biomedical and Health Informatics , vol. 24, no. 2, pp. 414 -423, 2020. DOI: 10.1109/JBHI.2 019.2931842

[50] Linares -Espinos E., Hernandez V., Dominguez - Escrig J., Fernandez -Pello S., and et al., “Methodology of a Systematic Review ,” Actas Urologicas Espanolas (English Edition) , vol. 42, no. 8, pp. 499 -506, 2018. DOI: 10.1016/j.acuro.2018.01.010

[51] Lindn er N., Kuwabara A., and Holt T., “Non - Invasive and Minimally Invasive Glucose Monitoring Devices: A Systematic Review and Meta -Analysis on Diagnostic Accuracy of Hypoglycaemia Detection ,” Systematic Reviews , vol. 10, no. 1, pp. 1 -14, 2021. DOI: 10.1186/s13 643 -021 -01644 -2

[52] Liu K., Li L., Ma Y., Jiang J., and et al., “Machine Learning Models for Blood Glucose Level Prediction in Patients with Diabetes Mellitus: Systematic Review and Network Meta -Analysis ,” JMIR Medical Informatics , vol. 11, no. e47833, pp. 1-30, 2023. DOI: 10.2196/47833

[53] Man C., Micheletto F., Lv D., Breton M., and et al., “The UVA/PADOVA Type 1 Diabetes Simulator: New Features ,” Journal of Diabetes Science and Technology , vol. 8, no. 1, pp. 26 -34, 2014. DOI: 10.1177/1932296813514502

[54] Marling C. and Bunescu R., “The OhioT1DM Dataset for Blood Glucose Level Prediction: Update 2020,” CEUR Workshop Proceedings , vol. 2675, pp. 71 -74, 2020. https://webpages.charlotte.edu/rbunescu/data/ohi ot1dm/OhioT1DM -dataset.html

[55] Martinez -Delgado L., Munoz -Organero M., and Queipo -Alvarez P., “Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions ,” Sensors , vol. 21, no. 16, pp. 1 -29, 2021. https://doi.org/10.3390/s21165273

[56] Mayer -Davis E., Lawrence J., Dab elea D., Divers J., and et al “Incidence Trends of Type 1 and Type 2 Diabetes Among Youths, 2002 -2012 ,” New England Journal of Medicine , vol. 376, no. 15, pp. 1419 -1429, 2017 DOI: 10.1056/NEJMoa1610187

[57] McShinsky R and Marshall B., “Comparison of Forecasting Algorithms for Type 1 Diabetic Glucose Prediction on 30 and 60 -Minute Prediction Horizons ,” in Proceedings of the 5 th International Workshop on Knowledge Discovery in Healthcare Data (KDH@ECAI) , vol, 2675, pp. 12 -18, 2020. https://ceur -ws.org/V ol- 2675/paper2.pdf

[58] Medanki S., Dommati N., Bodapati H., Katru V., and et at., “Artificial Intelligence Powered Glucose Monitoring and Controlling System: Pumping Module ,” World Journal of Experimental Medicine , vol. 14, no. 1, pp. 1 -13, 2024. DOI: 10.5493/ wjem.v14.i1.87916

[59] Mohebbi A., Johansen A., Hansen N., Christensen P., and et al., “Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data ,” in Proceedings of the 42 nd Annual International Conference of the IEEE Engineering in Medic ine and Biology Society , Quebec, pp. 5140 -5145, 2020. https://doi.org/10.1109/EMBC44109.2020.91766 95

[60] Mosquera -Lopez C and Jacobs P., “Incorporating Glucose Variability into Glucose Forecasting Accuracy Assessment Using the New Glucose Variability Impact In dex and the Prediction Consistency Index: An LSTM Case Example ,” Journal of Diabetes Science and Technology , vol. 16, no. 1, pp. 7 -18, 2022. DOI: 10.1177/19322968211042621

[61] Mosquera -Lopez C., Dodier R., Tyler N., Wilson L., and et al., “Predicting and Preve nting Nocturnal Hypoglycemia in Type 1 Diabetes Using Big Data Analytics and Decision Theoretic Analysis ,” Diabetes Technology and Therapeutics , vol. 22, no. 11, pp. 801 -811, 2020. DOI: 10.1089/dia.2019.0458

[62] Oviedo S., Contreras I., Quiros C., Gimenez M., and et al., “Risk -Based Postprandial Hypoglycemia Forecasting Using Supervised Learning ,” International Journal of Medical Informatics , vol. 126, pp. 1 -8, 2019. DOI: 10.1016/j.ijmedinf.2019.03.008

[63] Pappada S., Cameron B., Rosman P., Bourey R., and et al., “Neural Network -Based Real -Time Prediction of Glucose in Patients with Insulin - Dependent Diabetes ,” Diabetes Technology and Therapeutics , vol. 13, no. 2, pp. 135 -141, 2011. DOI: 10.1089/dia.2010.0104

[64] Parcerisas A., Contreras I., Delecourt A., Bertachi A., a nd et al., “A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients Under Multiple Doses of Insulin ,” Sensors , vol. 22, no. 4, pp. 1 -14, 2022. DOI: 10.3390/s22041665

[65] Perez -Gandia C., Facchinetti A., Sparacino G., Cobelli C., and et al., “Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring ,” Diabetes Technology and Therapeutics , vol. 12, no. 1, pp. 81 -88, 2010. DOI: 10.1089/dia.2009.0076

[66] Plis K., Bunescu R.., Marling C., Shubrook J., and Schwartz F., “A Machine Learning Approach to Predicting Blood Glucose Levels for Diabetes Management ,” in Proceedings of the AAAI Workshop: Modern Artificial Intelligence for Health Analytics , Quebec , pp. 35 -39, 2014. https://api.semant icscholar.org/CorpusID:1727404 4

[67] Pohanka M., “Quartz Crystal Microbalance (QCM) Sensing Materials in Biosensors Development ,” International Journal of Electrochemical Science , vol. 16, no. 12, pp.1 -10, 2021. https://doi.org/10 .20964/2021.12.15

[68] Prendin F., Del Favero S., Vettoretti M., Sparacino G., and Facchinetti A., “Forecasting of Glucose Levels and Hypoglycemic Events: Head -to-Head Comparison of Linear and Nonlinear Data -Driven Algorithms Based on Continuous Glucose Monitor ing Data Only ,” Sensors , vol. 21, no. 5, pp. 1-21, 2021. https://doi.org/10.3390/s21051647

[69] Prendin F., Pavan J., Cappon G., Del Favero S., and et al, “The Importance of Interpreting Machine Learning Models for Blood Glucose Prediction in Diabetes: An Analy sis Using SHAP ,” Scientific reports , vol. 13, no. 1, pp. 1 -14, 2023. DOI: 10.1038/s41598 -023 -44155 -x

[70] Ramachandran A., “Know the Signs and Symptoms of Diabetes ,” Indian Journal of Medical Research , vol. 140, no. 5, pp. 579 -581, 2014. https://www.researchgate.net/publication/270825 871_Know_the_signs_and_symptoms_of_diabet es

[71] Ramalingaswamy Cheruku D.and Kuppili V., “An Optimized and Efficient Radial Basis Neural Network Using Cluster Validity Index for Diabetes Classification ,” The Inter national Arab Journal of Information Technology , vol. 16, no. 5, pp. 816 -826, 2019.

[72] Rashid M., Askari M., Chen C., Liang Y., and et al., “Artificial Intelligence Algorithms for Treatment of Diabetes ,” Algorithms , vol. 15, no. 9, pp. 1 -20, 2022. DOI: 10.339 0/a15090299

[73] Rashid M., Samadi S., Sevil M., Hajizadeh I., and et al., “Simulation Software for Assessment of Nonlinear and Adaptive Multivariable Control Algorithms: Glucose -Insulin Dynamics in Type 1 Diabetes ,” Computers and Chemical Engineering , vol. 130 , 2019. DOI: 10.1016/j.compchemeng.2019.106565

[74] Reddy R., Resalat N., Wilson L., Castle J., and et al., “Prediction of Hypoglycemia During Aerobic Exercise in Adults with Type 1 Diabetes ,” Journal of Diabetes Science and Technology , vol. 13, no. 5, pp. 919 -927, 2019. DOI: 10.1177/1932296818823792

[75] Resalat N., El Youssef J., Tyler N., Castle J., and Jacobs P., “A Statistical Virtual Patient Population for the Glucoregulatory System in Type 1 Diabetes with Integrated Exercise Model ,” Plos One , vol. 14, no. 7, pp. 1 -11, 2019. DOI: 10.1371/journal.pone.0217301

[76] Rodriguez Leon C., Banos O., Fernandez Mora O., Martinez Bedmar A., and et al., “Prediction of Blood Glucose Levels in Patients with Type 1 Diabetes via LSTM Neural Networks,” in proceeding of the 17 th Inte rnational Work - Conference on Artificial Neural Networks , Ponta Delgada, pp. 563 -573, 2023. DOI: https://doi.org/10.1007/978 -3-031 -43085 -5_45

[77] Rodriguez -Leon C., Aviles -Perez M., Banos O., Quesada -Charneco M., and et al, “T1DiabetesGranada: a longitudinal mu lti-modal dataset of type 1 diabetes mellitus ,” Scientific Data , vol. 10, no. 1, pp. 1 -11, 2023. https://doi.org/10.1038/s41597 -023 -02737 -4

[78] Ruan Y., Bellot A., Moysova Z., Tan G., and et al., “Predicting the Risk of Inpatient Hypoglycemia with Machine Learning Using Electronic Health Records ,” Diabetes Care , vol. 43, no. 7, pp. 1504 - 1511, 2020. DOI: 10.2337/dc19 -1743

[79] Sampa M., Biswas T., Rahman M., Aziz N., and et al., “A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninv asive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study ,” JMIR diabetes , vol. 8, no. 5, pp. 1 -11, 2023. DOI: 10.2196/49113

[80] Sampath S., Tkachenko P., Renard E., and Pereverzev S., “Glycemic Control Indices and Their Aggre gation in the Prediction of Nocturnal Hypoglycemia from Intermittent Blood Glucose Measurements ,” Journal of Diabetes Science and Technology , vol. 10, no. 6, pp. 1245 -1250, 2016. DOI: 10.1177/1932296816670400

[81] Seo W., Lee Y., Lee S., Jin S., and Park S., “A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin ,” BMC Medical Informatics and Decision Making , vol. 19, no. 1, pp. 1 -13, 2019. DOI: 1006 The International Arab Journal of Information Technology, Vol. 23, No. 5, September 2026 10.1186/s12911 -019 -0943 -4

[82] Smith J., Everhar t J., Dickson W., Knowler W., and Johannes R., “Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus ,” in Proceedings of the Symposium on Computer Applications and Medical Care, IEEE Computer Society Press , Washington, pp. 261 -265, 1988. https://pmc.ncbi.nlm.nih.gov/articles/PMC22453 18/

[83] Srinivasu P., Ahmed S., Hassaballah M., and Almusallam N., “An Explainable Artificial Intelligence Software System for Predicting Diabetes ,” Heliyon , vol. 10, no. 16, pp. 1 -19, 2024. https://doi.org/1 0.1016/j.heliyon.2024.e36112

[84] Stuart K., Adderley N., Marshall T., Rayman G., and et al., “Predicting Inpatient Hypoglycaemia in Hospitalized Patients with Diabetes: A Retrospective Analysis of 9584 Admissions with Diabetes ,” Diabetic Medicine , vol. 34, no. 10, pp. 1385 -1391, 2017. DOI: 10.1111/dme.13409

[85] Subramanian A., Adap S., Chawale S., Singh S., and Sudhakaran P., “Non Invasive Glucose Measurement Using Raman Spectroscopy ,” International Research Journal of Engineering and Technology , vol. 4, no. 03, pp . 2872 -2875, 2017. https://www.irjet.net/archives/V4/i3/IRJET - V4I3722.pdf

[86] Sudharsan B., Peeples M., and Shomali M., “Hypoglycemia Prediction Using Machine Learning Models for Patients with Type 2 Diabetes ,” Journal of Diabetes Science and Technology , vol. 9, no. 1, pp. 86 -90, 2015. DOI: 10.1177/1932296814554260

[87] Tang L., Chang S., Chen C., and Liu J., “Non - Invasive Blood Glucose Monitoring Technology: A Review ,” Sensors , vol. 20, no. 23, pp. 1 -32, 2020. DOI: 10.3390/s20236925

[88] Tidepool, “Tidepool Big Data Don ation Data Set,” https://support.tidepool.org/hc/en - us/articles/360044350552 -Exporting -Tidepool - data , Last Visited, 2025.

[89] Wang J., “Electrochemical Glucose Biosensors ,” Chemical Reviews , vol. 108, no. 2, pp. 814 -825, 2008. https://doi.org/10.1021/cr068123a

[90] Wang Y., “A Comparison of Machine Learning Algorithms in Blood Glucose Prediction for People with Type 1 Diabetes ,” in Proceedings of the 2 nd International Symposium on Artificial Intelligence for Medicine Sciences , Beijing, pp. 351 -360, 2021. https://doi.org/10.1145/3500931.3500993

[91] Wilinska M., Chassin L., Acerini C., Allen J., and et al., “Simulation Environment to Evaluate Closed -Loop Insuli n Delivery Systems in Type 1 Diabetes ,” Journal of Diabetes Science and Technology, vol. 4, no. 1, pp. 132 -144, 2010. DOI: 10.1177/193229681000400117

[92] Yismaw M., Tafere C., Tefera B., Demsie D., and et al., “Artificial Intelligence Based Predictive Tools fo r Identifying Type 2 Diabetes Patients at High Risk of Treatment Non -Adherence: A Systematic Review ,” International Journal of Medical Informatics , vol. 198, pp. 105858, 2025. DOI: 10.1016/j.ijmedinf.2025.105858

[93] Zecchin C., Facchinetti A., Sparacino G., an d Cobelli C., “Jump Neural Network for Real -Time Prediction of Glucose Concentration ,” Methods in Molecular Biology , vol. 1260, pp. 245 -259, 2015. DOI: 10.1007/978 -1-4939 -2239 -0_15

[94] Zecchin C., Facchinetti A., Sparacino G., De Nicolao G., and Cobelli C., “Neural network incorporating meal information improves accuracy of short -time prediction of glucose concentration ,” IEEE Transactions on Biomedical Engineering , vol. 59, no. 6, pp. 1550 -1560, 2012. DOI: 10.1109/TBME.2012.2188893

[95] Zhang M., Flores K., and Tra n H., “Deep Learning and Regression Approaches to Forecasting Blood Glucose Levels for Type 1 Diabetes ,” Biomedical Signal Processing and Control , vol. 69, pp. 102923, 2021. DOI: 10.1016/j.bspc.2021.102923

[96] Zhu T., Li K., Chen J., Herrero P., and Georgiou P ., “Dilated Recurrent Neural Networks for Glucose Forecasting in Type 1 Diabetes ,” Journal of Healthcare Informatics Research , vol. 4, no. 3, pp. 308 -324, 2020. DOI: 10.1007/s41666 -020 -00068 - 2

[97] Zhu T., Uduku C., Li K., Herrero P., and Georgiou P., “Enhancing self -management in type 1 diabetes with wearables and deep learning ,” NPJ Digital Medicine , vol. 5, no. 78, pp. 1 -11, 2022. DOI: 10.1038/s41746 -022 -00626 -5