Explainable diabetes prediction using a stacked ensemble framework
Saher Fatima Awan, Kainat Irfan, Umair Muneer Butt, Sukumar Letchmunan, Fadratul Hafinaz Hassan · 2026
Read Explainable diabetes prediction using a stacked ensemble framework by Saher Fatima Awan, Kainat Irfan, Umair Muneer Butt, Sukumar Letchmunan, Fadratul Hafinaz Hassan (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13577529.txt.
Description
Saher Fatima Awan and co-authors build an explainable stacked model to predict diabetes, in a study from two thousand twenty-six. Early spotting of diabetes can head off heart and kidney damage. Past models chased accuracy while leaving their reasoning opaque.
Three base learners feed a boosting meta-learner in the stack. Scaling, feature ranking, and synthetic balancing prepared the classic Pima data set. Accuracy hit eighty-six percent, beating recent rivals. Two explanation tools then showed which features drove each call, locally and globally. The authors pitch the pairing for trustworthy clinic use.
Catalog record
| CREATOR | Saher Fatima Awan, Kainat Irfan, Umair Muneer Butt, Sukumar Letchmunan, Fadratul Hafinaz Hassan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |