The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
IBM Auto AI Bot: Diabetes Mellitus Prediction Using Machine Learning Algorithms
Abstract
Chronic diseases such as diabetes can have a devastating effect on a person's overall well-being. Diabetes is characterised by abnormally high blood glucose levels, which are the result of either impaired insulin secretion or impaired physiologic effects, or both. Many tissues, including the eyes, kidneys, heart, blood vessels, and nerves, can be permanently damaged or dysfunctional due to diabetes. Type 1 diabetes (T1D) and type 2 diabetes are the two types of diabetes (T2D). Diabetics with type 1 diabetes tend to be younger people, with the majority of them being under the age of 30. Increased thirst and frequent urination are the most common symptoms, as are elevated blood glucose levels. Oral drugs alone are ineffective in treating this kind of diabetes, hence insulin therapy is essential. Middle-aged and older adults are more likely to develop type-2 diabetes, which is frequently linked to obesity, hypertension, dyslipidaemia, arteriosclerosis, and other conditions. Implementing a machine learning model that can quickly uncover the principles for predicting diabetes mellitus in individuals depending on their health status is discussed in this research. There must be an IBM cloud deployment of the model before it can be utilised as an API in web app development. The User Interface will display the model's prediction.
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Cites
Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers
Hasan, M. K., Alam, M. A., Das, D., Hossain, E., & Hasan, M. (2020) · IEEE Access · doi:10.1109/access.2020.2989857 · Cited by 656
Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus
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Prediction of Diabetes Empowered With Fused Machine Learning
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Random Forest Algorithm for the Prediction of Diabetes
Vijiyakumar, K., Lavanya, B., Nirmala, I., et al. (2019) · IEEE International Conference on System, Computation, Automation and Networking (ICSCAN) · doi:10.1109/icscan.2019.8878802 · Cited by 152
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Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review
Khan, F. A., Zeb, K., Al-Rakhami, M. S., Derhab, A., & Bukhari, S. A. C. (2021) · IEEE Access · doi:10.1109/access.2021.3059343 · Cited by 125
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Diabetes Prediction Using Different Machine Learning Algorithms
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A Review of Trustworthy and Explainable Artificial Intelligence (XAI)
Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023) · IEEE Access · doi:10.1109/access.2023.3294569 · Cited by 352
“This system was made by IBM, and used by Sunganthi et al. and many others to predict different medical diseases which are nearly impossible to predict by humans alone [36], [37], [39], [40].”
Method
Metadata
- Authors
- Kumar, J. N. V. R. S., Kumar, K. H., Haleem, A., Sivaranjani, B., Kiran, B. N. T., & Prameela, S.
- Affiliations
- Vellore Institute of Technology University (IN)
- Venue
- 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC)
- Publisher
- IEEE
- Published
- 2022-05-09
- Pages
- 24-29
- OpenAlex
- W4282973093
- Cited by
- 8
- References
- 14
- Citations by year
- 2026: 2 · 2024: 1 · 2023: 4 · 2022: 1
- FWCI
- 1.39
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- diabetes prediction, type 1 diabetes, type 2 diabetes, blood glucose, automated machine learning, cloud deployment, web application, diabetes risk prediction
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