The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Deep learning approach for diabetes prediction using PIMA Indian dataset
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Projections of Global Mortality and Burden of Disease from 2002 to 2030
Mathers, C. D., & Loncar, D. (2006) · PLoS Medicine · doi:10.1371/journal.pmed.0030442 · Cited by 11,386 · cited 1×
“Diabetes has steadily been listed at the top position for a major cause of death in America [3].”
BackgroundThe Emerging Risk Factors Collaboration (2010) · The Lancet · doi:10.1016/s0140-6736(10)60484-9 · Cited by 5,125
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Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076
Cardiovascular disease risk profiles
Anderson, K. M., Odell, P. M., Wilson, P. W. F., & Kannel, W. B. (1991) · American Heart Journal · doi:10.1016/0002-8703(91)90861-b · Cited by 2,163
Obesity, Metabolic Syndrome, and Cardiovascular Disease
Grundy, S. M. (2004) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2004-0372 · Cited by 1,371
International Journal of Scientific and Technology Research
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YALE: rapid prototyping for complex data mining tasks
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., & Euler, T. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150531 · Cited by 1,010
Artificial neural networks in medical diagnosis
Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013) · Journal of Applied Biomedicine · doi:10.2478/v10136-012-0031-x · Cited by 898
Cited by
Machine-learning-based disease diagnosis: a comprehensive review
Ahsan, M. M., Luna, S. A., & Siddique, Z. (2022) · Healthcare · doi:10.3390/healthcare10030541 · Cited by 603
Gupta, H., Varshney, H., Sharma, T. K., Pachauri, N., & Verma, O. P. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00398-7 · Cited by 191
Metadata
- Authors
- Naz, H., & Ahuja, S.
- Venue
- Journal of Diabetes & Metabolic Disorders
- Published
- 2020-04-14
- Volume
- 19
- Issue
- 1
- Pages
- 391-403
- ISSN
- 2251-6581 (ISSN-L)
- OpenAlex
- W3017382074
- Cited by
- 348
- References
- 38
- FWCI
- 35.25
- Type
- journal article
- Language
- en
- Open access
- green
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- Naz, H., & Ahuja, S. (2020). Deep learning approach for diabetes prediction using PIMA Indian dataset. Journal of Diabetes & Metabolic Disorders. https://doi.org/10.1007/s40200-020-00520-5
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
