Prediction of Risk for Life-Threatening Cancers and Chronic Diseases Using Machine Learning Driven by Routine Blood Markers

Introduction:
Early identification of individuals at risk for cancers and chronic diseases remains a major challenge in clinical practice. Conventional risk assessment tools rely on limited variables, threshold-based interpretations, or imaging modalities, which restrict scalability and early detection. This study proposes a machine learning–based framework for large-scale risk stratification using only routine blood biomarkers and basic demographic variables.

Methods:
We developed a series of supervised machine learning models for over 80 chronic diseases and cancers using more than five million de-identified electronic health record datasets. Input features included complete blood count parameters, differential leukocyte counts, metabolic panel components, lipid profiles, age, and biological sex. Longitudinal laboratory data up to three years prior to diagnosis were analyzed. Models were trained using a 70/30 train-test split, with performance evaluated using area under the receiver operating characteristic curve (AUROC). Model interpretability was achieved through feature importance ranking and mechanistic mapping of biomarkers to known biological pathways.

Results:
The framework generated disease-specific risk scores, confidence scores, and ranked biomarker contributions. Representative case studies in chronic kidney disease and lung cancer demonstrated biologically plausible patterns, including renal dysfunction markers, inflammatory signatures, and paraneoplastic indicators. The models identified multivariate physiological signatures consistent with early disease processes, even in the absence of overt abnormalities in individual laboratory values.

Conclusions:
Routine blood biomarkers, when analyzed through machine learning, capture integrated systemic physiological signals that enable early risk stratification across multiple diseases. This approach offers a scalable, interpretable, and cost-effective strategy for population-level screening and targeted diagnostic triage. Prospective validation is warranted to assess clinical utility and real-world impact.