Stokes SH. Preston, Deaths attributable to diabetes in the United States:comparison of data sources and estimation approaches. PLoS ONE. 2017;12(1):e0170219.

Article  PubMed  PubMed Central  Google Scholar 

Elflein J. Estimated number of diabetics worldwide in 2021, 2030, and 2045, 2022 [Online]. Available at https://www.statista.com/statistics/271442/number-of-diabetics-worldwide/. Accessed 27 Sep, 2022.

Riddle MC, Herman WH. The cost of diabetes care—an elephant in the room. Diabetes Care. 2018;41(5):929–32.

Article  PubMed  Google Scholar 

Kaur H, Kumari V. Predictive modelling and analytics for diabetes using a machine learning approach. ACI. 2020;18(1/2):90–100. https://doi.org/10.1016/j.aci.2018.12.004.

Article  Google Scholar 

Lu H, Uddin S, Hajati F, Moni MA, Khushi A. A patient network-based machine learning model for disease prediction: the case of type 2 diabetes mellitus. Appl Intell. 2022;52(3):2411–22. https://doi.org/10.1007/s10489-021-02533-w.

Article  Google Scholar 

Sarwar MA, Kamal N, Hamid W, Shah MA. Prediction of diabetes using ma- chine learning algorithms in healthcare. In: 2018 24th International Conference on Automation and Computing (ICAC) Newcastle Upon Tyne, United Kingdom. 2018;1–6. https://doi.org/10.23919/IConAC.2018.8748992.

Sneha N, Gangil T. Analysis of diabetes mellitus for early prediction using optimal features selection. J Big Data. 2019;6(1):13. https://doi.org/10.1186/s40537-019-0175-6.

Article  Google Scholar 

Hasan MK, Alam MA, Das D, Hossain E, Hasan M. Diabetes predictionusing ensembling of different machine learning classifiers. IEEE Access. 2020;8:76516–31.

Article  Google Scholar 

Han L, Luo S, Yu J, Pan L, Chen S. Rule extraction from support vectormachines using ensemble learning approach: an application for diagnosis ofdiabetes. IEEE J Biomed Health Inf. 2014;19(2):728–34.

Article  Google Scholar 

Ramesh J, Aburukba R, Sagahyroon A. A remote healthcare monitoringframework for diabetes prediction using machine learning. Healthc Technol Lett. 2021;8(3):45–57.

Article  PubMed  PubMed Central  Google Scholar 

Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetesprediction. ICT Express. 2021;7(4):432–9.

Article  Google Scholar 

Goyal P, Jain S. Prediction of type-2 diabetes using classification and ensemblemethod approach. In 2022 International Mobile and Embedded Technology Conference (MECON). IEEE. 2022;658–665.

Gupta H, Varshney H, Sharma TK, et al. Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction. Complex Intell Syst. 2022;8:3073–87. https://doi.org/10.1007/s40747-021-00398-7.

Article  Google Scholar 

Chang V, Ganatra MA, Hall K, Golightly L, Xu QA. An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators. Healthcare Analytics. 2022. https://doi.org/10.1016/j.health.2022.100118.

Article  PubMed  Google Scholar 

Rawat V, Suryakant S. A classification system for diabetic patients with machine learning techniques. International Journal of Mathematical Engineering and Management Sciences. 2019;4(3):729–44. https://doi.org/10.33889/IJMEMS.2019.4.3-057.

Article  Google Scholar 

Yang L, Hsieh C-K, Yang H, et al. Yum-me: a personalized nutrient-based meal recommender system. ACM Trans Inf Syst. 2017;36(1):1–31.

Article  Google Scholar 

Sahin S, Merotra J, Kang J, Trenell M, Catt M, Yu EH. Simultaneous electrochemical detection of glucose and nonesterified fatty acids (NEFAs) for diabetes management. IEEE Sens J. 2018;18(22):9075–80.

Article  Google Scholar 

Georga E, Protopappas V, Guillen A et al. Data mining for blood glucose prediction and knowledge discovery in diabetic patients: the METABO diabetes modeling and management system. In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2009;5633–36. Minneapolis, MN, USA.

Ahuja R, Sharma SC, Ali M. A diabetic disease prediction model based on classification algorithms. Ann EmergTechnolComput. 2019;3(3):44–52. https://doi.org/10.33166/AETiC.2019.03.005.

Article  Google Scholar 

Zhao J, Gao H, Yang C, An T, Kuang Z, Shi L. Attention-oriented CNN method for type 2 diabetes prediction. Appl Sci. 2024;14:3989. https://doi.org/10.3390/app14103989.

Article  Google Scholar 

Alkanhel RI, Saleh H, Elaraby A, Alharbi S, Elmannai H, Alaklabi S, et al. Hybrid CNN-GRU model for real-time blood glucose forecasting: enhancing IoT-based diabetes management with AI. Sensors (Basel). 2024;24(23):7670. https://doi.org/10.3390/s24237670.

Article  PubMed  Google Scholar 

El-Bashbishy A, El-Bakry H. Pediatric diabetes prediction using deep learning. Sci Rep. 2024. https://doi.org/10.1038/s41598-024-51438-4.

Article  PubMed  PubMed Central  Google Scholar 

Zhao J, Gao H, Sun L, et al. Type 2 diabetes prediction method based on dual-teacher knowledge distillation and feature enhancement. Sci Rep. 2025;15:133. https://doi.org/10.1038/s41598-024-83902-6.

Article  PubMed  PubMed Central  Google Scholar 

Rustam F, Al-Shamayleh AS, Shafique R, et al. Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model. Sci Rep. 2024;14(1):23274. https://doi.org/10.1038/s41598-024-74357-w.

Article  PubMed  PubMed Central  Google Scholar 

Chowdhury MM, Ayon RS, Hossain MS. An investigation of machine learning algorithms and data augmentation techniques for diabetes diagnosis using class imbalanced BRFSS dataset. Healthcare Analytics. 2024;5(100297):2772–4425. https://doi.org/10.1016/j.health.2023.100297.

Article  Google Scholar 

Aslan MF, Sabanci K. A novel proposal for deep learning-based diabetes prediction: converting clinical data to image data. Diagnostics. 2023;13(4):796. https://doi.org/10.3390/diagnostics13040796.

Article  PubMed  PubMed Central  Google Scholar 

Dharmarathne G, Jayasinghe TN, Bogahawaththa M, Meddage DPP, Rathnayake U. A novel machine learning approach for diagnosing diabetes with a self-explainable interface. Healthcare Analytics. 2024;5(100301):2772–4425. https://doi.org/10.1016/j.health.2024.100301.

Article  Google Scholar 

Iparraguirre-Villanueva O, Espinola-Linares K, Flores Castañeda RO, Cabanillas-Carbonell M. Application of machine learning models for early detection and accurate classification of type 2 diabetes. Diagnostics (Basel). 2023;13(15):2383. https://doi.org/10.3390/diagnostics13142383.

Article  PubMed  Google Scholar 

Wang, Yong & Lu, Yurun & Liu, Dan & Liang, Zhongming & Liu, Yitong & Chen, Pei & Liu, Rui & Feng, Zhanying & Li, Lei & Sheng, Bin & Jia, Weiping & Chen, Luonan & Li, Huating. A generative pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data. (2024). https://doi.org/10.21203/rs.3.rs-3932671/v1

Chowdhary CL, Acharjya DP. Segmentation and feature extraction in medicalimaging: a systematic review. Procedia Comput Sci. 2020;167:26–36.

Article  Google Scholar 

Harvey HB, Sotardi ST. The pareto principle. J Am Coll Radiol. 2018;15(6):931.

Article  PubMed  Google Scholar