XGBoost-Powered Digital Twins Leverage Routine Blood Tests for Early Detection of Cancer and Cardiovascular Disease

TL;DR

This study develops machine learning models called digital blood twins using routine blood tests and demographic data to enable scalable, low-cost early detection of cancer and cardiovascular diseases, demonstrating promising accuracy and interpretability.

Contribution

The paper introduces digital blood twins powered by XGBoost that leverage routine blood tests for scalable disease screening, a novel approach for accessible early detection.

Findings

Colorectal cancer prediction ROC-AUC up to 0.993

Cardiovascular disease models show ROC-AUC around 0.813

Models demonstrate potential for scalable, low-cost screening

Abstract

Early detection of cancer and cardiovascular diseases is fundamental to improving patient outcomes and reducing healthcare expenditure. Current cancer screening programs are targeted towards specific cancers and are often inaccessible to large parts of the population, particularly in remote regions. This project aimed to develop digital blood twins: machine learning models that leverage routinely collected blood test data, demographics, comorbidities, and prescribed medications, for scalable and cost-effective disease screening. Digital blood twins were constructed using the UK Biobank dataset (n = 373,269). Using age, sex, comorbidities, medication profiles, and blood test z-scores, three iterations of XGBoost classifiers were trained for broad cancer, colorectal cancer, and cardiovascular disease prediction. Model interpretability was achieved through SHAP and dimensionality reduction analyses (UMAP, t-SNE). Broad-category cancer models achieved ROC-AUC = 0.607-0.706. Colorectal cancer prediction demonstrated excellent discrimination (ROC-AUC = 0.816-0.993), and cardiovascular models showed clinical utility, notably for hypertension (ROC-AUC = 0.813, F1 = 0.861). SHAP revealed consistent importance of age, sex, basophil count, and cystatin C. Immune digital blood twins as an agnostic tool demonstrate proof-of-concept feasibility for accessible, low-cost, and scalable screening of cancer and cardiovascular diseases, supporting future integration into predictive and preventive healthcare.