Paper Citation Record · LEDGER

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights

As of 21 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2505.05683.

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

pith.paper-citation-record.v1
2505.05683 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:02:31.002827Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:34:59.182599Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T23:35:08.109592Z

Reference resolution

22 of 22 outbound references displayed

verified exact4
verified fuzzy15
unresolved2
parse uncertain0
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metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a8cae726-328d-4f88-8dc1-1ae023e08c49 · outbound

This paper cites Diabetes diagnosis through machine learning: Investigating algorithms and data augmentation for class imbalanced BRFSS dataset,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Diabetes diagnosis through machine learning: Investigating algorithms and data augmentation for class imbalanced BRFSS dataset,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.540263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 33c93130-03ca-448e-b337-833db41b21f7 · outbound

This paper cites Acomparativestudy ofmachinelearningapproachesfordiabetesriskprediction:Insightsfrom SHAP and feature importance,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Acomparativestudy ofmachinelearningapproachesfordiabetesriskprediction:Insightsfrom SHAP and feature importance,

Reference 2

Resolution: verified exact; raw_fallback, observed 2026-08-15T23:02:31.344348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 17095219-6323-4019-9df5-4ca80deb7296 · outbound

This paper cites A comparative analysis of LIME and SHAP interpreters with explainable ML-baseddiabetespredictions,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights A comparative analysis of LIME and SHAP interpreters with explainable ML-baseddiabetespredictions,

Reference 3

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T23:02:31.261426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0a0d36a8-7fcd-4a70-8060-ef3c1c51fff0 · outbound

This paper cites A comparative study of diabetes prediction based on lifestyle factors using machine learning,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights A comparative study of diabetes prediction based on lifestyle factors using machine learning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.528033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation abc35133-fb5b-44b3-8e57-b7e4eb6a98e7 · outbound

This paper cites EvaluationofExplainable Artificial Intelligence: SHAP, LIME, and CAM,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights EvaluationofExplainable Artificial Intelligence: SHAP, LIME, and CAM,

Reference 5

Resolution: verified exact; raw_fallback, observed 2026-08-15T23:02:31.197733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c160483a-80d1-4c3c-9b55-7ac9a76a2a04 · outbound

This paper cites Predictingthe Risk of Diabetes Using Explainable Artificial Intelligence,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Predictingthe Risk of Diabetes Using Explainable Artificial Intelligence,

Reference 6

Resolution: verified exact; raw_fallback, observed 2026-08-15T23:02:31.124175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 36a69285-f262-4d1c-af24-28ad65f78739 · outbound

This paper cites Explainable AI for healthcare: A study for interpretingdiabetesprediction,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Explainable AI for healthcare: A study for interpretingdiabetesprediction,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.515637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e1fffcd8-b9e2-4e02-a3bb-ff7b75c1638f · outbound

This paper cites A comparison of instance-level counterfactual explanation algorithms for behavioral and textualdata:SEDC,LIME-CandSHAP-C,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights A comparison of instance-level counterfactual explanation algorithms for behavioral and textualdata:SEDC,LIME-CandSHAP-C,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.502971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 471f4ba6-af04-4ada-b942-d60107ff2946 · outbound

This paper cites A diabetes predictionmodelwithvisualizedexplainableartificialintelligence(XAI) technology,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights A diabetes predictionmodelwithvisualizedexplainableartificialintelligence(XAI) technology,

Reference 9

Resolution: verified exact; doi, observed 2026-08-15T23:02:31.035104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 58392ee8-0c59-4d8e-84d7-d1d3746be2c7 · outbound

This paper cites Correlation based breast cancer detectionusingmachinelearning,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Correlation based breast cancer detectionusingmachinelearning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.489885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3bd31834-504f-4ba7-9c3c-762f0155d129 · outbound

This paper cites A decision support system for diabetes prediction using machine learning and deep learning techniques,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights A decision support system for diabetes prediction using machine learning and deep learning techniques,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.476320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 72d55879-5227-44d9-acef-68c0c65a9676 · outbound

This paper cites Diabetes prediction using machine learningalgorithms,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Diabetes prediction using machine learningalgorithms,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.463402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 64ab5627-519b-4c2d-87cc-2a2df874f881 · outbound

This paper cites Machine learning tools for long-term type 2 diabetes risk prediction,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Machine learning tools for long-term type 2 diabetes risk prediction,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.450707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 731dd127-7f78-4a4f-9135-b81d4ed8e1f4 · outbound

This paper cites Prediction of diabetes usingmachinelearningalgorithms in healthcare,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Prediction of diabetes usingmachinelearningalgorithms in healthcare,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.438622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 43a869d4-c715-4eb2-8c6c-894d375878b1 · outbound

This paper cites Primary stage of diabetes prediction using machine learning approaches,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Primary stage of diabetes prediction using machine learning approaches,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.426458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c074f398-6211-434d-8a1b-d7973c86d2ba · outbound

This paper cites Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T23:02:30.979359Z

Source-reported events for the cited work

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Observation ad691bbf-1ffe-41ac-a6ec-bb6c826e7922 · outbound

This paper cites The rising burden of non-communicable diseasesinsub-SaharanAfrica,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights The rising burden of non-communicable diseasesinsub-SaharanAfrica,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.414665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3396f6c4-eaaf-43ed-8bcd-40b8f7d1fd95 · outbound

This paper cites Random forests,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Random forests,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T23:02:30.987054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6660e946-acf0-46bb-be7d-f0dd5cdcd792 · outbound

This paper cites Gestationaldiabetesmellitus,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Gestationaldiabetesmellitus,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.395223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7fd135a6-a27c-4e13-88f7-fb37e9c906fd · outbound

This paper cites Managementandpreventionstrategiesfornon-communicable diseases(NCDs)andtheirriskfactors,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Managementandpreventionstrategiesfornon-communicable diseases(NCDs)andtheirriskfactors,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.383061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bca29ed2-69d3-4ce9-9586-9dedb648a5c1 · outbound

This paper cites Handling class imbalance in customer churn prediction,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Handling class imbalance in customer churn prediction,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.370924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 04cbcaf9-c29d-4fc3-aa9c-524bcc06a643 · outbound

This paper cites Aging, diabetes, and the public health system in the United States,.

Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights Aging, diabetes, and the public health system in the United States,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:02:31.358109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 25850c8e-64cb-4beb-b9b1-7544b708a178 · inbound

Trustworthy Chronic Disease Risk Prediction For Self-Directed Preventive Care via Medical Literature Validation cites this paper.

Trustworthy Chronic Disease Risk Prediction For Self-Directed Preventive Care via Medical Literature Validation Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:35:08.144674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.