Enhanced diabetes prediction using CTGAN-MLP approach on body composition data
Javad Hassannataj Joloudari, Mohammad Maftoun, Mohammad Ali Nematollahi, Kandala N. V. P. S. Rajesh, S. Prasanth Vaidya, Kamireddy Rasool Reddy, Pirhossein Kolivand · 2025
Read Enhanced diabetes prediction using CTGAN-MLP approach on body composition data by Javad Hassannataj Joloudari, Mohammad Maftoun, Mohammad Ali Nematollahi, Kandala N. V. P. S. Rajesh, S. Prasanth Vaidya, Kamireddy Rasool Reddy, Pirhossein Kolivand (2025) 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-12808130.txt.
Description
Javad Hassannataj Joloudari and co-authors published a study in 2025 on predicting diabetes from body measures. Early warning of diabetes risk lets doctors act in time. Body makeup data, like fat share and lean mass, carry clues, but true cases are rare in data sets, which skews learning.
The team used a generative model to mint extra fake cases that mimic real ones. That balanced the data better than simple copying tricks. A neural network then learned from the enriched set. It scored about ninety-four percent accuracy under repeated cross-checks, the best among the models tried. An explanation tool ranked fat share, fat-free mass, and resting energy burn as top clues. The authors say the pairing of fake data and network gives a sharp, readable risk tool.
Catalog record
| CREATOR | Javad Hassannataj Joloudari, Mohammad Maftoun, Mohammad Ali Nematollahi, Kandala N. V. P. S. Rajesh, S. Prasanth Vaidya, Kamireddy Rasool Reddy, Pirhossein Kolivand |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 73 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |