Gradient boosting classifier

Approach · Other

Supervised ML (questionnaire features)

Supervised machine-learning classifier (gradient boosting, PyCaret) trained on 4,240 questionnaires of the same clinic; not a language model — listed as the study's comparator.

Model

Gradient boosting classifier Katsuki et al. 2023 model

Other approaches

An approach outside the categories above; see the method details.

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External dataset · Katsuki-1818

Katsuki et al. headache clinic questionnaire test cohort

Hold-out test cohort of 1,818 patients of a Japanese headache-specialty clinic: a 22-item structured questionnaire per patient, specialist-confirmed diagnosis. Five ICHD-3-based classes instead of single diagnoses: migraine and/or medication-overuse headache, tension-type headache, TACs, other primary headaches, secondary headaches. Real patients, strongly imbalanced (1,463 migraine/MOH). Results are therefore given at group level only; MOH is merged with migraine, so the classes do not match the ICHD-3 groups exactly.

Cases

1818

Language

Japanese

Cases available

No, not public

Source

Katsuki M, Moran K, O'Connor S, et al. Comparative diagnostic agreement of a supervised machine learning model and a general-purpose, zero-shot, non-domain-adapted large language model for classifying headache disorders using structured questionnaires. Cephalalgia 46(4) (2026). https://doi.org/10.1177/03331024261441574

Results on this dataset are not directly comparable with results on a platform release or on another dataset.

About this result

5-class accuracy 1531/1818, computed from the confusion matrix (Table 2); macro sensitivity 48.5 %, macro F 54.3 %.

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