The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia.

Thrombosis is one of the most frequent complications of cancer, with a potential impact on morbidity and mortality, particularly those with acute myeloid leukemia (AML). Therefore, effective thrombosis prevention is a crucial aspect of cancer management. However, preventive measures against thrombosis may carry inherent risks and complications. Consequently, the application of thrombosis prevention should be limited to patients with a reasonable risk of developing thrombosis. This thesis explores the potential of data science (DS) methods for predicting venous thrombosis in patients with acute myeloid leukemia. In order to ascertain which patients are at risk, statistical and machine-learning (ML) algorithms were employed to predict which patients with leukemia will develop thrombosis. Multilayer Perceptron (MLP) was found to be the best fit among the models evaluated, achieving the C statistic of 0.749. We examined which attributes are significant and what role they play in prediction and found six significant parameters: sex of the patient, prior history of thrombotic event, type of therapy, international normalized ratio (INR), Eastern Cooperative Oncology Group (ECOG) performance status, and Hematopoietic Cell Transplantation-specific Comorbidity. These findings suggest that subtle DS techniques can improve the prediction of Thrombosis in AML patients, thereby aiding in individual treatment planning.