Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)
Open accessAbstract
Diabetes mellitus (DM) is a severe chronic disease that affects human health and has a high prevalence worldwide. Research has shown that half of the diabetic people throughout the world are unaware that they have DM and its complications are increasing, which presents new research challenges and opportunities. In this paper, we propose a preemptive diagnosis method for diabetes mellitus (DM) to assist or complement the early recognition of the disease in countries with low medical expert densities. Diabetes data are collected from the Zewditu Memorial Hospital (ZMHDD) in Addis Ababa, Ethiopia. Light Gradient Boosting Machine (LightGBM) is one of the most recent successful research findings for the gradient boosting framework that uses tree-based learning algorithms. It has low computational complexity and, therefore, is suited for applications in limited capacity regions such as Ethiopia. Thus, in this study, we apply the principle of LightGBM to develop an accurate model for the diagnosis of diabetes. The experimental results show that the prepared diabetes dataset is informative to predict the condition of diabetes mellitus. With accuracy, AUC, sensitivity, and specificity of 98.1%, 98.1%, 99.9%, and 96.3%, respectively, the LightGBM model outperformed KNN, SVM, NB, Bagging, RF, and XGBoost in the case of the ZMHDD dataset.
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LightGBM has advantages of faster training speed, higher efficiency, lower memory usage, better accuracy, and has been consistently outperforming other models.
“Similarly, LightGBM191 has the advantages of faster training speed, higher efficiency, lower memory usage, better accuracy192, and has been consistently outperforming other models193,194.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [192]
The Overview of Proposed Approach (§3.1) explicitly lists faster training, higher efficiency, lower memory use, and better accuracy as LightGBM advantages. The Comparison (§4.2) finds that LightGBM outperformed six other methods on the ZMHDD diabetes dataset, but does not establish that it consistently outperforms other models across settings. (LLM, conf=0.88)
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Cited by
Sorted by citations
Multimodal machine learning in precision health: A scoping review
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- Authors
- Rufo, D. D., Debelee, T. G., Ibenthal, A., & Negera, W. G.
- Affiliations
- Dilla University (ET)
- Addis Ababa Science and Technology University (ET)
- University of Applied Sciences and Arts Hildesheim/Holzminden/Göttingen (DE)
- Ethiopian Artificial Intelligence Center, Addis Ababa 40782, Ethiopia
- Corresponding author
- Ibenthal, Achim
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2021-09-19
- Volume
- 11
- Issue
- 9
- Pages
- 1714
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC8467876
- OpenAlex
- W3199944191
- Cited by
- 317
- References
- 43
- Citations by year
- 2026: 58 · 2025: 91 · 2024: 86 · 2023: 54 · 2022: 25 · 2021: 3
- FWCI
- 36.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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 › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- diabetes mellitus, diabetes diagnosis, LightGBM, gradient boosting, early detection, tree-based classification, Ethiopia, resource-limited settings
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