Error Reduction in Leukemia Machine Learning Classification With Conformal Prediction.
Journal:
JCO clinical cancer informatics
Published Date:
May 28, 2025
Abstract
PURPOSE: Recent advances in machine learning have led to the development of classifiers that predict molecular subtypes of acute lymphoblastic leukemia (ALL) using RNA-sequencing (RNA-seq) data. Although these models have shown promising results, they often lack robust performance guarantees. The aim of this study was three-fold: to quantify the uncertainty of these classifiers, to provide prediction sets that control the false-negative rate (FNR), and to perform implicit error reduction by transforming incorrect predictions into uncertain predictions.