Machine Learning in the Diagnosis and Prognostic Assessment of Acute Myeloid Leukemia

Acute Myeloid Leukemia (AML) is a heterogeneous hematological malignancy characterized by the uncontrolled proliferation of immature myeloid cells in the bone marrow and peripheral blood. Early diagnosis and accurate prognostic assessment are essential for selecting appropriate treatment strategies and improving patient survival. Machine learning enables the analysis of complex clinical, laboratory, genomic, and imaging data with high predictive accuracy. This study investigates the application of machine learning models in the diagnosis and prognostic assessment of AML. The findings demonstrate that advanced machine learning algorithms improve diagnostic accuracy, relapse prediction, and individualized risk stratification.