Development and Internal Validation of a Machine Learning-Based Model for Thalassemia Identification Using Complete Blood Count Parameters
Ping Yin, Jialin Tan, Honghai Hong · 2026
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Description
Ping Yin and co-authors train learning models to spot thalassemia risk from a routine blood count, in a 2026 paper. Gene tests prove the blood disorder but cost too much for wide screening, while plain rules lack aim. The team drew records from more than eighteen hundred people.
From more than two hundred lab values, twelve facts were kept for the models. Seven learners all sorted well, and a forest model led the pack. It caught near nine in ten true cases while clearing most non-cases. Three red cell facts drove its calls the most: cell size, cell pigment load, and size spread. The authors say the model can flag who merits a gene test, pending outside proof.
Catalog record
| CREATOR | Ping Yin, Jialin Tan, Honghai Hong |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 31 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |