Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A feasibility study

TL;DR

This study explores the feasibility of using machine learning models to diagnose Primary Immune Thrombocytopenia (ITP) from routine blood tests and demographic data, highlighting the potential and limitations of ML in clinical diagnosis.

Contribution

It demonstrates that decision tree and random forest models can effectively diagnose ITP with high accuracy and fairness, emphasizing the importance of model choice and data features.

Findings

Decision Tree and Random Forest achieved high predictive accuracy.

Models without demographic data had better accuracy but lower fairness.

Platelet count was the most significant variable for diagnosis.

Abstract

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.