Machine-Learning-Based Diabetes Mellitus Risk Prediction Using Multi-Layer Neural Network No-Prop Algorithm Article Swipe
J. Jeba Sonia
,
J. Prassanna
,
Abdul Quadir
,
Senthilkumar Mohan
,
Arun Kumar Sivaraman
,
Kong Fah Tee
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics13040723
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics13040723
Over the past few decades, the prevalence of chronic illnesses in humans associated with high blood sugar has dramatically increased. Such a disease is referred to medically as diabetes mellitus. Diabetes mellitus can be categorized into three types, namely types 1, 2, and 3. When beta cells do not secrete enough insulin, type 1 diabetes develops. When beta cells create insulin, but the body is unable to use it, type 2 diabetes results. The last category is called gestational diabetes or type 3. This happens during the trimesters of pregnancy in women. Gestational diabetes, however, disappears automatically after childbirth or may continue to develop into type 2 diabetes. To improve their treatment strategies and facilitate healthcare, an automated information system to diagnose diabetes mellitus is required. In this context, this paper presents a novel system of classification of the three types of diabetes mellitus using a multi-layer neural network no-prop algorithm. The algorithm uses two major phases in the information system: the training phase and the testing phase. In each phase, the relevant attributes are identified using the attribute-selection process, and the neural network is trained individually in a multi-layer manner, starting with normal and type 1 diabetes, then normal and type 2 diabetes, and finally healthy and gestational diabetes. Classification is made more effective by the architecture of the multi-layer neural network. To provide experimental analysis and performances of diabetes diagnoses in terms of sensitivity, specificity, and accuracy, a confusion matrix is developed. The maximum specificity and sensitivity values of 0.95 and 0.97 are attained by this suggested multi-layer neural network. With an accuracy score of 97% for the categorization of diabetes mellitus, this proposed model outperforms other models, demonstrating that it is a workable and efficient approach.
Related Topics
Concepts
Gestational diabetes
Diabetes mellitus
Context (archaeology)
Type 2 diabetes
Artificial neural network
Medicine
Algorithm
Computer science
Artificial intelligence
Confusion matrix
Type 2 Diabetes Mellitus
Pregnancy
Disease
Machine learning
Internal medicine
Endocrinology
Gestation
Biology
Paleontology
Genetics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics13040723
- https://www.mdpi.com/2075-4418/13/4/723/pdf?version=1676531923
- OA Status
- gold
- Cited By
- 24
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4320918908
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4320918908Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/diagnostics13040723Digital Object Identifier
- Title
-
Machine-Learning-Based Diabetes Mellitus Risk Prediction Using Multi-Layer Neural Network No-Prop AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-14Full publication date if available
- Authors
-
J. Jeba Sonia, J. Prassanna, Abdul Quadir, Senthilkumar Mohan, Arun Kumar Sivaraman, Kong Fah TeeList of authors in order
- Landing page
-
https://doi.org/10.3390/diagnostics13040723Publisher landing page
- PDF URL
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https://www.mdpi.com/2075-4418/13/4/723/pdf?version=1676531923Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2075-4418/13/4/723/pdf?version=1676531923Direct OA link when available
- Concepts
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Gestational diabetes, Diabetes mellitus, Context (archaeology), Type 2 diabetes, Artificial neural network, Medicine, Algorithm, Computer science, Artificial intelligence, Confusion matrix, Type 2 Diabetes Mellitus, Pregnancy, Disease, Machine learning, Internal medicine, Endocrinology, Gestation, Biology, Paleontology, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
24Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 10, 2023: 6Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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