Open Access
Research Article
Integrating natural therapeutics in machine learning models for diabetes mellitus optimization
Mona Adlakhamonaadlakham@gmail.comDepartment of Computer Science and Engineering School 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 Engineering School of Engineering Sciences and Technology (SEST)Jamia Hamdard, Delhi, 110062, India0000-0002-4677-711XView full profile → , Sameena Naazsameena.naaz@roehampton.ac.ukDepartment of Computer Science University of RoehamptonLondon, SW15 5PJ, U.K.0000-0003-0080-5063View full profile → , *Sherin ZafarCorresponding authorsherin.zafar@jamiahamdard.ac.inDepartment of Computer Science and Engineering School 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:
- 01 Jan 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2182
- Pages:
- 371–398
Abstract
Diabetes Mellitus is a rapidly growing global health concern that poses significant risks, if not detected and managed at an early stage. This study aims to develop a model that accurately predicts diabetes mellitus using machine learning (ML) models, namely hybrid and ensemble models. Various natural remedies that have traditionally been used to manage diabetes have also been studied. Standalone machine learning algorithms were combined as hybrid and ensemble models. Performance metrices were used to evaluate the models. This integrated approach is a complete package for diabetes management, reducing the overall cost of this life-long disease.
Keywords
Subject Classifications
92B20 - Neural networksartificial intelligence for/ in biological
References
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