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pith:OIT4KSOS

pith:2026:OIT4KSOSYJ2S6G76ID32RQD5UI

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A Unified Three-Stage Machine Learning Framework for Diabetes Detection, Subtype Discrimination, and Cognitive-Metabolic Hypothesis Testing

Rishav Tewari, Ruzina Haque Laskar, Vishal Pandey

A three-stage machine learning framework detects diabetes, clusters subtypes without labels, and links better glycaemic control to higher cognitive scores.

arxiv:2605.13464 v1 · 2026-05-13 · cs.LG

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The findings support the utility of statistically grounded and interpretable ML pipelines for reproducible diabetes analytics and subtype-aware exploratory analysis.

C2weakest assumption

That the low silhouette score of approximately 0.116 still indicates clinically plausible subtype partitions, and that the NCSU and Ohio datasets are representative without unstated biases affecting predictions or the reported association.

C3one line summary

A three-stage ML framework achieves 0.825 ROC-AUC for diabetes detection, identifies two subtypes via clustering on glucose insulin and age, and finds a significant positive correlation (rho=0.208) between glycaemic control and cognitive function.

References

17 extracted · 17 resolved · 0 Pith anchors

[1] Standards of medical care in diabetes --- 2021 2021

[2] M. A. Atkinson, G. S. Eisenbarth, and A. W. Michels. Type 1 diabetes. The Lancet, 383(9911):69--82, 2014 2014

[3] S. M. de la Monte and J. R. Wands. Alzheimer's disease is type 3 diabetes --- evidence reviewed. Journal of Diabetes Science and Technology, 2(6):1101--1113, 2008 2008

[4] I. Feinkohl, J. F. Price, M. W. Strachan, and B. M. Frier. The impact of diabetes on cognitive decline: potential vascular, metabolic, and psychosocial risk factors. Alzheimer's & Dementia, 11(8):970- 2015

[5] IDF Diabetes Atlas, 10th ed 2021

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T02:44:41.658949Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7227c549d2c2752f1bfe40f7a8c07da235b624006febfcc341a485dee09525a6

Aliases

arxiv: 2605.13464 · arxiv_version: 2605.13464v1 · doi: 10.48550/arxiv.2605.13464 · pith_short_12: OIT4KSOSYJ2S · pith_short_16: OIT4KSOSYJ2S6G76 · pith_short_8: OIT4KSOS

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/OIT4KSOSYJ2S6G76ID32RQD5UI \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 7227c549d2c2752f1bfe40f7a8c07da235b624006febfcc341a485dee09525a6

Canonical record JSON

{
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    "abstract_canon_sha256": "63565e5e2e7b035445ab14a795da3d59adc9bfda21887c076c0afbb5c76942d3",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T12:53:39Z",
    "title_canon_sha256": "0b68bdaeaf72940302025077f1101308190a052dbdd74a761e89eb767b55300d"
  },
  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
    "version": 1
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}