Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling
Md. Shaheenur Islam Sumon, Marwan Malluhi, Noushin Anan, Mohannad Natheef AbuHaweeleh, Hubert Krzyslak, Semir Vranic, Muhammad E. H. Chowdhury, Shona Pedersen · 2024
Read Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling by Md. Shaheenur Islam Sumon, Marwan Malluhi, Noushin Anan, Mohannad Natheef AbuHaweeleh, Hubert Krzyslak, Semir Vranic, Muhammad E. H. Chowdhury, Shona Pedersen (2024) 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-11727543.txt.
Description
Shaheenur Islam Sumon and co-authors stack several machine learning models to tell small cell lung cancer from its non-small cell cousin using blood chemistry profiles, in a study from 2024. Small cell cancer spreads fast and kills quickly, yet diagnosis still leans on invasive tests. The team profiled metabolites from 191 small cell patients, 173 non-small cell patients, and 97 healthy controls.
A stacked ensemble, where one model learns from the others' outputs, sorted all three groups with 85 percent accuracy. Head-to-head between the two cancers, a tree ensemble reached 88 percent. An explanation method flagged benzoic acid, lactate, and the amino acid arginine as the strongest signals. The authors pitch the blood test as a gentler path to early subtyping.
Catalog record
| CREATOR | Md. Shaheenur Islam Sumon, Marwan Malluhi, Noushin Anan, Mohannad Natheef AbuHaweeleh, Hubert Krzyslak, Semir Vranic, Muhammad E. H. Chowdhury, Shona Pedersen |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |