Patient ‐based prediction algorithm of relapse after allo‐HSCT for acute Leukemia and its usefulness in the decision‐making process using a machine learning approach

We focused on the construction of a prediction model for the bedside decision ‐making process and investigated the usefulness of machine learning (ML). Clinicians can refer to the model which was constructed by ML and select treatment options. ML may improve the decision‐making process for therapy in the diversified allo‐HSCT field. AbstractAlthough allogeneic hematopoietic stem cell transplantation (allo ‐HSCT) is a curative therapy for high‐risk acute leukemia (AL), some patients still relapse. Since patients simultaneously have many prognostic factors, difficulties are associated with the construction of a patient‐based prediction algorithm of relapse. The alternating decision tree (ADTree) is a successful classification method that combines decision trees with the predictive accuracy of boosting. It is a component of machine learning (ML) and has the capacity to simultaneously analyze multiple factors. Using ADTree, we attempted to construct a prediction model of leukemia relapse with in 1 year of transplantation. With the model of training data (n = 148), prediction accuracy, the AUC of ROC, and the κ‐statistic value were 78.4%, 0.746, and 0.508, respectively. The false positive rate (FPR) of the relapse prediction was as low as 0.134. In an evaluation of the model with va lidation data (n = 69), prediction accuracy, AUC, and FPR of the relapse prediction were similar at 71.0%, 0.667, and 0.216, respectively. These results suggest that the model is ge...
Source: Cancer Medicine - Category: Cancer & Oncology Authors: Tags: ORIGINAL RESEARCH Source Type: research