Diagnosis of complications of type 2 diabetes based on weighted multi-label small sphere and large margin machine
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Wang et al. (2021) reported only accuracy.
“Wang et al. (2021) reported only accuracy.”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Wang et al. (2021)
In Wang et al. (2021), the accuracy for covering all three complications at once was 0.685 ± 0.000 for BR (linear), 0.701 ± 0.000 for BR (poly), 0.701 ± 0.000 for BR (rbf), 0.520 ± 0.001 for WML-SSLM (linear), 0.661 ± 0.002 for WML-SSLM (poly), and 0.697 ± 0.002 for WML-SSLM (rbf).
“BR (linear) resulted in accuracy of 0.685 ± 0.000, BR (poly) of 0.701 ± 0.000, BR (rbf) of 0.701 ± 0.000, WML‐SSLM (linear) of 0.520 ± 0.001, WML‐SSLM (poly) of 0.661 ± 0.002 and WML‐SSLM (rbf) of 0.697 ± 0.002.”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Wang et al. (2021)
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“…on our systematic review, we found that models most often predict the risk of developing diabetic neuropathy ( n = 7) (Dagliati et al., 2018; Fan et al., 2021; Lagani et al., 2015; Liu et al., 2020; Ljubic et al., 2020; Ozdemir et al., 2020; Wang et al., 2021) and diabetic nephropathy ( n = 6).”
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- Authors
- Wang, H., Xu, Y., Chen, Q., & Wang, X.
- Affiliations
- China Agricultural University (CN)
- Chinese People's Liberation Army (CN)
- Corresponding author
- Xu, Yitian
- Venue
- Applied Intelligence
- Published
- 2020-08-07
- Volume
- 51
- Issue
- 1
- Pages
- 223-236
- ISSN
- 0924-669X (ISSN-L)
- 1573-7497
- OpenAlex
- W3048232032
- Cited by
- 9
- References
- 43
- Citations by year
- 2025: 2 · 2023: 1 · 2022: 3 · 2021: 3
- FWCI
- 1.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Intelligence (publishedVersion)
- Funders
- National Natural Science Foundation of China (11671010)
- Natural Science Foundation of Beijing Municipality (4172035)
- General Hospital of People’s Liberation Army (2017MBD-002)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- type 2 diabetes, diabetes complications, multi-label classification, binary relevance, class imbalance, macrovascular complications, microvascular complications, diabetic neuropathy, feature selection
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