  Primary Immune thrombocytopenia (ITP) is a rare autoimmune disease
characterised by immune-mediated destruction of peripheral blood platelets in
patients leading to low platelet counts and bleeding. The diagnosis and
effective management of ITP is challenging because there is no established test
to confirm the disease and no biomarker with which one can predict the response
to treatment and outcome. In this work we conduct a feasibility study to check
if machine learning can be applied effectively for diagnosis of ITP using
routine blood tests and demographic data in a non-acute outpatient setting.
Various ML models, including Logistic Regression, Support Vector Machine,
k-Nearest Neighbor, Decision Tree and Random Forest, were applied to data from
the UK Adult ITP Registry and a general hematology clinic. Two different
approaches were investigated: a demographic-unaware and a demographic-aware
one. We conduct extensive experiments to evaluate the predictive performance of
these models and approaches, as well as their bias. The results revealed that
Decision Tree and Random Forest models were both superior and fair, achieving
nearly perfect predictive and fairness scores, with platelet count identified
as the most significant variable. Models not provided with demographic
information performed better in terms of predictive accuracy but showed lower
fairness score, illustrating a trade-off between predictive performance and
fairness.
