Paper Citation Record · LEDGER

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection

As of 14 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2508.00117.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.00117 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:25:43.604413Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

13 of 13 outbound references displayed

verified exact1
verified fuzzy11
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aeed48e6-739b-4608-91f4-85d3c8a1bfac · outbound

This paper cites Liver disease in numbers – key facts and statistics,.

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection Liver disease in numbers – key facts and statistics,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:25:43.857695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0e57e392-abd8-42ff-93b2-fa39cbdcbaa1 · outbound

This paper cites Diagnosis and treatment of liver disease: Current trends and future directions,.

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection Diagnosis and treatment of liver disease: Current trends and future directions,

Reference 2

Resolution: verified exact; doi, observed 2026-08-06T10:25:43.658363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1091378a-4055-42da-aac7-d704ebb21113 · outbound

This paper cites Unveiling explainable ai in healthcare: Current trends, challenges, and future directions,.

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection Unveiling explainable ai in healthcare: Current trends, challenges, and future directions,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T10:25:43.546899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eddce6cb-55da-45ac-aabc-80d490d12227 · outbound

This paper cites Improving chronic kidney disease detection efficiency: Fine tuned catboost and nature-inspired algorithms with explainable ai,.

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection Improving chronic kidney disease detection efficiency: Fine tuned catboost and nature-inspired algorithms with explainable ai,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:25:43.840703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e648f29c-f1bf-475e-8040-5e72201f1f38 · outbound

This paper cites Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches,.

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:25:43.822065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 823cdcd3-baa8-4375-ae8a-be07a1db0f55 · outbound

This paper cites Adaptive method for exploring deep learning tech - niques for subtyping and prediction of liver disease,.

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection Adaptive method for exploring deep learning tech - niques for subtyping and prediction of liver disease,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:25:43.804630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 81587a02-6f78-49c1-9187-57c8dbc21512 · outbound

This paper cites Explainability enhanced liver disease diagnosis technique using tree selection and stacking ensemble -based random forest model,.

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection Explainability enhanced liver disease diagnosis technique using tree selection and stacking ensemble -based random forest model,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:25:43.785933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b825bd0d-4f41-464c-b8af-21905d3dcce4 · outbound

This paper cites Performance analysis of machine learning models for liver disease patient classification,.

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection Performance analysis of machine learning models for liver disease patient classification,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:25:43.769192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c7822533-6791-4d57-81f6-1baa88882feb · outbound

This paper cites A comparative study of machine learning algorithms using explainable artificial intelligence system for predicting liver disease,.

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection A comparative study of machine learning algorithms using explainable artificial intelligence system for predicting liver disease,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:25:43.748212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3c8eddb3-b5a7-4985-8057-0ff931da01de · outbound

This paper cites Prediction of chronic liver disease patients using integrated projection based statistical feature extraction with machine learning algorithms,.

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection Prediction of chronic liver disease patients using integrated projection based statistical feature extraction with machine learning algorithms,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:25:43.729382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f915ee05-ee4f-400d-93b5-94295bb74726 · outbound

This paper cites Liver disease patient dataset,.

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection Liver disease patient dataset,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:25:43.712102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2cacc1bf-9c4b-464f-be4c-6e05832b4281 · outbound

This paper cites Analysis of variance (anova),.

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection Analysis of variance (anova),

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:25:43.694648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e36c1b30-f4d2-4ce5-a9a5-7689edc5b889 · outbound

This paper cites Recursive feature elimination with cross - validation with decision tree: Feature selection method for machine learning-based intrusion detection systems,.

StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection Recursive feature elimination with cross - validation with decision tree: Feature selection method for machine learning-based intrusion detection systems,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:25:43.676924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.