Integrating natural therapeutics in machine learning models for diabetes mellitus optimization
Mona Adlakhamonaadlakham@gmail.comDepartment of Computer Science and EngineeringSchool of Engineering Sciences and Technology (SEST)Jamia Hamdard, Delhi, 110062, India0009-0009-6278-8271View full profile → , M. Afshar Alamaalam@jamiahamdard.ac.inDepartment of Computer Science and EngineeringSchool of Engineering Sciences and Technology (SEST)Jamia Hamdard, Delhi, 110062, India0000-0002-4677-711XView full profile → , Sameena Naazsameena.naaz@roehampton.ac.ukDepartment of Computer ScienceUniversity of RoehamptonLondon, SW15 5PJ, U.K.0000-0003-0080-5063View full profile → , *Sherin ZafarCorresponding authorsherin.zafar@jamiahamdard.ac.inDepartment of Computer Science and EngineeringSchool of Engineering Sciences and Technology (SEST)Jamia Hamdard, Delhi, 110062, India0000-0001-6656-1095View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 06 Aug 2025
- Published Online:
- 05 Jan 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2182
- Pages:
- 371–398
Abstract
Keywords
Subject Classifications
References
[1] B. Zhou et al., “Worldwide trends in diabetes prevalence and treatment from 1990 to 2022: a pooled analysis of 1108 population-representative studies with 141 million participants,” The Lancet, vol. 404, no. 10467, pp. 2077–2093 (Nov. 2024), doi: 10.1016/S0140-6736(24)02317-1.
[2] M. Balbaa, M. El-Zeftawy, and S. A. Abdulmalek, “Therapeutic screening of herbal remedies for the management of diabetes,” Nov. 02 (2021), MDPI. doi: 10.3390/molecules26226836.
[3] A. H. Afzal, O. Alam, S. Zafar, A. Alam, and J. Khan, “Exploring the Anti-Diabetic Potential of Cichorium intybus through Integrated Network Pharmacology Analysis and Molecular Docking Validation,” Journal of Natural Remedies, vol. 24, no. 10, pp. 2253–2261 (Oct. 2024), doi: 10.18311/jnr/2024/44047.
[4] S. M. Bhatt and N. Sharma, “A Comprehensive Review on the Role of <i>Gymenma sylvestre</i> in Combating Diabetes-2,” Journal of Natural Remedies, pp. 2143–2155 (Nov. 2024), doi: 10.18311/jnr/2024/44121.
[5] K. N. Sharma and A. Kashyap, “Review of herbal and natural remedies for diabetes in traditional Indian medicine,” (2023). [Online]. Available: https://www.virtued.in/s/pages/screji
[6] Z. Sitobo, L. Tinotenda Navhaya, and X. Henry Makhoba, “Medicinal plants as a source of natural remedies in the management of diabetes,” INNOSC Theranostics and Pharmacological Sciences, vol. 7, no. 3, p. 1885 (Jul. 2024), doi: 10.36922/itps.1885.
[7] N. Vinothkumar et al., “Deep learning and probabilistic neural networks-based identification and classification of Pneumonia-related lung diseases,” Journal of Information and Optimization Sciences, vol. 46, no. 1, pp. 213–222 (2025), doi: 10.47974/jios-1865.
[8] S. L. Cichosz, C. Bender, and O. Hejlesen, “Explainable Machine Learning-Based Approach to Identify People at Risk of Diabetes Using Physical Activity Monitoring,” BioMedInformatics, vol. 5, no. 1 (Mar. 2025), doi: 10.3390/biomedinformatics5010001.
[9] L. M. S. Akoosh, F. Siddiqui, S. Zafar, S. Naaz, and M. A. Alam, “Enhancing Heart Health Prediction with Natural Remedies Through Integration of Hybrid Deep Learning Models,” Journal of Natural Remedies, vol. 24, no. 11, pp. 2481–2491 (Nov. 2024), doi: 10.18311/jnr/2024/44045.
[10] M. Adlakha, S. Naaz, S. Zafar, and M. A. Alam, “Deep Learning Approach for Accurate Prediction of diabetes,” in Proceedings of the 5th International Conference on Information Management & Machine Intelligence, New York, NY, USA: ACM, pp. 1–6 (Nov. 2023). doi: 10.1145/3647444.3647832.
[11] M. Adlakha, M. A. Alam, S. Zafar, and S. Naaz, “Prediction Optimization for Type 2 Diabetes Mellitus Using Artificial Neural Networks,” in Proceedings of the 6th International Conference on Communication and Intelligent Systems, Springer Nature (2025). doi: 10.1007/978-981-96-5723-0_2.
[12] K. Oliullah, M. H. Rasel, M. M. Islam, M. R. Islam, M. A. H. Wadud, and M. Whaiduzzaman, “A stacked ensemble machine learning approach for the prediction of diabetes,” J Diabetes Metab Disord, vol. 23, no. 1, pp. 603–617 (Jun. 2024), doi: 10.1007/s40200-023-01321-2.
[13] M. A. Rahim, M. A. Hossain, M. N. Hossain, J. Shin, and K. S. Yun, “Stacked Ensemble-Based Type-2 Diabetes Prediction Using Machine Learning Techniques,” Annals of Emerging Technologies in Computing, vol. 7, no. 1, pp. 30–39 (2023), doi: 10.33166/AETiC.2023.01.003.
[14] Md. Rashed, Md. I. Hossain, A. Mahdi, and G. Mustofa, “Hybrid Machine Learning Models for Accurate Type 2 Diabetes Mellitus Prediction Using a Stacking Classifier and a Meta-Model Approach,” Cureus Journal of Computer Science (Mar. 2025), doi: 10.7759/s44389-025-03135-0.
[15] M. Nagassou, R. W. Mwangi, and E. Nyarige, “A Hybrid Ensemble Learning Approach Utilizing Light Gradient Boosting Machine and Category Boosting Model for Lifestyle-Based Prediction of Type-II Diabetes Mellitus,” Journal of Data Analysis and Information Processing, vol. 11, no. 04, pp. 480–511 (2023), doi: 10.4236/jdaip.2023.114025.
[16] O. Olabanjo, A. Wusu, O. Olabanjo, M. Asokere, O. Afisi, and B. Akinnuwesi, “A novel deep learning model for early diabetes risk prediction using attention-enhanced deep belief networks with highly imbalanced data,” International Journal of Information Technology (Singapore), vol. 17, no. 4, pp. 1933–1955 (May 2025), doi: 10.1007/s41870-025-02459-3.
[17] P. Chowdhury, P. Barua, and M. N. Uddin, “Diabetes Prediction Using Machine Learning and Hybrid Deep Learning Ensemble Technique,” in 2024 IEEE Conference on Computing Applications and Systems, COMPAS 2024, Institute of Electrical and Electronics Engineers Inc. (2024). doi: 10.1109/COMPAS60761.2024.10796486.
[18] R. K. Sharma and M. Jailia, “Machine learning and IoT-based garbage detection system for smart cities,” Journal of Information and Optimization Sciences, vol. 44, no. 3, pp. 393–406 (2023), doi: 10.47974/jios-1349.
[19] P. R. Anisha and K. Anjali, “CyberShield-H: A hybrid sequential deep learning model for robust DDoS mitigation in critical healthcare IoT systems,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 7, pp. 2889–2898 (Oct. 2025), doi: 10.47974/JDMSC-2559.
[20] S. Nimbagal, A. Sarangmath, M. Unakal, and A. Gongade, “Factors influencing the acceptance of mobile health apps among Indians : A behavioral perspective,” Journal of Statistics and Management Systems, vol. 28, no. 8, pp. 1433–1442 (2025), doi: 10.47974/JSMS-1506.
[21] A. Kumar, K. M. Bhagat, P. Dadheech, S. R. Dogiwal, and R. Sharma, “Manifold learning and differential geometry for disease detection and risk prediction,” Journal of Interdisciplinary Mathematics, vol. 28, no. 6, pp. 2059–2069 (2025), doi: 10.47974/jim-2347.
[22] D. Samuel and M. Anita Rajam, “MXLPred: A Natural Language Processing Deep Learning Model for ICU Patients’ Data,” The International Arab Journal of Information Technology, vol. 22, no. 4 (2025), doi: 10.34028/iajit/22/4/12.
[23] “Clerodendrum glandulosum.” Accessed: Sep. 25 (2025). [Online]. Available: https://en.wikipedia.org/wiki/Clerodendrum_glandulosum
[24] S. Alam et al., “Antidiabetic Phytochemicals From Medicinal Plants: Prospective Candidates for New Drug Discovery and Development,” Feb. 24, , Frontiers Media S.A (2022). doi: 10.3389/fendo.2022.800714.
[25] Brittany Lubeck, “Everything You Need to Know About Maitake Mushroom.” Accessed: Sep. 25 (2025). [Online]. Available: https://www.verywellhealth.com/maitake-mushroom-benefits-nutrition-8606730
[26] M. A. S. Hairani, F. A. Abdul Majid, N. H. Zakaria, D. Hudiyanti, A. Fadhlina, and H. I. Sheikh, “Anti-diabetic properties of traditional herbal concoction containing Eleutherine palmifolia (L.) Merr., Momordica charantia L., and Syzygium polyanthum (Wight.): a bibliometric analysis,” Dec. 01 (2023), BioMed Central Ltd. doi: 10.1186/s43014-023-00172-x.
[27] Brittany Lubeck, “Nigella Sativa (Black Seed) and What It Can Do For Your Health.” Accessed: Sep. 25 (2025). [Online]. Available: https://www.verywellhealth.com/everything-you-should-know-about-nigella-sativa-7775388
[28] A. Charkravarty and G. Ramasamy, “Profiling Of Volatiles In Tissues Of Salacia Reticulata Wight. With Anti-Diabetic Potential Using GC-MS And Molecular Docking,”
[29] S. Deyno, K. Eneyew, S. Seyfe, and E. Wondim, “Efficacy, safety and phytochemistry of medicinal plants used for the management of diabetes mellitus in Ethiopia: a systematic review,” Clinical Phytoscience, vol. 7, no. 1 (Dec. 2021), doi: 10.1186/s40816-021-00251-x.
[30] “Pima Indians Diabetes Database,” Kaggle. Accessed: Jun. 20 (2025). [Online]. Available: https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database.
