Home ›
Entities
› academia
› DiaXplain: A transparent and interpretable artificial intelligence approach for Type-2 diabetes diagnosis through deep learning
DiaXplain: A transparent and interpretable artificial intelligence approach for Type-2 diabetes diagnosis through deep learning
DiaXplain: A transparent and interpretable artificial intelligence approach for Type-2 diabetes diagnosis through deep learning
Summary
DiaXplain: A transparent and interpretable artificial intelligence approach for Type-2 diabetes diagnosis through deep learning is a scholarly article[1].
Key Facts
DiaXplain: A transparent and interpretable artificial intelligence approach for Type-2 diabetes diagnosis through deep learning's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). DiaXplain: A transparent and interpretable artificial intelligence approach for Type-2 diabetes diagnosis through deep learning. Retrieved May 24, 2026, from https://4ort.xyz/entity/diaxplain-a-transparent-and-interpretable-artificial-intelligence-approach-for-type-2-diabetes-diagnosis-through-deep-le
MLA“DiaXplain: A transparent and interpretable artificial intelligence approach for Type-2 diabetes diagnosis through deep learning.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/diaxplain-a-transparent-and-interpretable-artificial-intelligence-approach-for-type-2-diabetes-diagnosis-through-deep-le.
BibTeX@misc{4ortxyz_diaxplain-a-transparent-and-interpretable-artificial-intelligence-approach-for-type-2-diabetes-diagnosis-through-deep-le_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{DiaXplain: A transparent and interpretable artificial intelligence approach for Type-2 diabetes diagnosis through deep learning}}, year = {2026}, url = {https://4ort.xyz/entity/diaxplain-a-transparent-and-interpretable-artificial-intelligence-approach-for-type-2-diabetes-diagnosis-through-deep-le}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): DiaXplain: A transparent and interpretable artificial intelligence approach for Type-2 diabetes diagnosis through deep learning — https://4ort.xyz/entity/diaxplain-a-transparent-and-interpretable-artificial-intelligence-approach-for-type-2-diabetes-diagnosis-through-deep-le (retrieved 2026-05-24)
