A Conformal Prediction Approach to Enhance Predictive Accuracy and Confidence in Machine Learning Application in Chronic Diseases.

Heterogeneity in chronic malignancies raises an increasing interest for the integration and study of predictive models. This study presents a machine learning model approach to predict outcomes and improve their trustworthiness in multi-factorial diseases with highly heterogeneous outcomes, like Chronic Lymphocytic Leukemia (CLL). We incorporated Conformal Prediction to quantify our models uncertainty, and generate confident personalized prediction outcomes that can be integrated into clinical practice.