Publication2025
Exploring Machine Learning Techniques for Predicting Brain Stroke from Heart Disease: Insights from ANN, NCF, and XG Boost
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- National
- Journal
- Studies in Computational Intelligence
- Volume
- 1191
- Pages
- 27-58
- Published
- 1 January 2025
- DOI
- 10.1007/978-3-031-82516-3_2
- Scopus ID
- 105000316818
- ISSN
- 1860949X
- Authors
- 5
- Open access
- No
- Citations
- 0
- 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 Ensure healthy lives and promote well-being for all at all ages
Machine learning models predict brain stroke risk from cardiac biomarkers and lipid profiles enabling early cardiovascular disease intervention
- 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
Artificial neural networks, neural collaborative filtering, and gradient boosting represent advanced AI methodology for health prediction
- 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-1.2 Health and well-being
Predictive healthcare system supports early disease detection and health outcomes improvement through data science
- 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. “Exploring Machine Learning Techniques for Predicting Brain Stroke from Heart Disease: Insights from ANN, NCF, and XG Boost”. Kortex.