Publication2024
Unraveling Diabetes Detection: A Comparative Study of Machine Learning Approaches
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- National
- Journal
- 2024 15th International Conference on Computing Communication and Networking Technologies Icccnt 2024
- Published
- 1 January 2024
- DOI
- 10.1109/icccnt61001.2024.10724390
- Scopus ID
- 85211104433
- Authors
- 6
- Open access
- No
- Citations
- 1
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
3.4 Reduce premature mortality from non-communicable diseases and promote mental health and well-being
Machine learning models for early diabetes detection achieve practical diagnostic accuracy, enabling timely intervention to prevent complications.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Comparative ML algorithm evaluation contributes to research in computational diagnostic tools.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
AI-based diagnostic system for diabetes detection represents digital health innovation advancing personalized medicine approaches.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Unraveling Diabetes Detection: A Comparative Study of Machine Learning Approaches”. Kortex.