  Acute Myeloid Leukemia (AML) is one of the most aggressive types of
hematological neoplasm. To support the specialists' decision about the
appropriate therapy, patients with AML receive a prognostic of outcomes
according to their cytogenetic and molecular characteristics, often divided
into three risk categories: favorable, intermediate, and adverse. However, the
current risk classification has known problems, such as the heterogeneity
between patients of the same risk group and no clear definition of the
intermediate risk category. Moreover, as most patients with AML receive an
intermediate-risk classification, specialists often demand other tests and
analyses, leading to delayed treatment and worsening of the patient's clinical
condition. This paper presents the data analysis and an explainable
machine-learning model to support the decision about the most appropriate
therapy protocol according to the patient's survival prediction. In addition to
the prediction model being explainable, the results obtained are promising and
indicate that it is possible to use it to support the specialists' decisions
safely. Most importantly, the findings offered in this study have the potential
to open new avenues of research toward better treatments and prognostic
markers.
