nsklab/anemia-sr-cbc-lightgbm

AI model by nsklab

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Introduction Published machine-learning models for anaemia detection from complete blood count (CBC) data routinely report accuracies between 94% and 99.8%. This one reports 91%, and that gap is the entire point of the model. Anaemia is not a disease that a model discovers in blood data — it is defined as a haemoglobin concentration below a threshold. So when a classifier is handed haemoglobin as an input feature and asked to predict a label that was generated by thresholding haemoglobin, it is not learning to detect disease. It is recovering an arithmetic cut-off that whoever assembled the dataset already knew. In the cohort behind this model, that degeneracy is total. A one-question decision tree using haemoglobin alone reproduces the label with 1.0000 accuracy, in training…

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  1. anemia-cbc-lightgbm
  2. Anaemia detection from seven routine complete blood count indices — with haemoglobin deliberately re

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nsklab/anemia-sr-cbc-lightgbm is a task-unspecified model repository published by nsklab on Hugging Face. A library was not reported.

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apache-2.0
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Created
2026-09-23T06:32:48.000Z
Last modified
2026-09-23T06:37:03.000Z

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