Can polycythaemia vera disease be predicted from haematologic parameters? A machine learning-based study.

ABSTRACT:

Aims

The aim of this research is to diagnose polycythaemia vera (PV) disease using different machine learning (ML) algorithms with complete blood count (CBC) parameters before further investigations such as Janus kinase 2 (JAK2), erythropoietin (EPO) and bone marrow biopsy (BMB).

Methods

The study included 1484 patients who presented to the adult haematology clinic with elevated haemoglobin. Participants were retrospectively screened for JAK2, EPO and BMB results, and patients were categorised as PV group (n=82) and non-PV (other) (n=1402). First, the synthetic minority oversampling technique (SMOTE) method was used to avoid data imbalance. Then, classification predictions were made using Random Forest, Support Vector Machine Technique, Extreme Gradient Boostin

SUBMITTER: Haskul M 

PROVIDER: S-EPMC12505070 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature