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Hydroxyurea (HU) has been shown to be effective in alleviating the symptomsof Sickle Cell Anemia disease. While Hydroxyurea reduces the complicationsassociated with Sickle Cell Anemia in some patients, others do not benefit fromthis drug and experience deleterious effects since it is also achemotherapeutic agent. Therefore, to whom, should the administration of HU beconsidered as a viable option, is the main question asked by the responsiblephysician. We address this question by developing modeling techniques that canpredict a patient's response to HU and therefore spare the non-responsivepatients from the unnecessary effects of HU on the values of 22 parameters thatcan be obtained from blood samples in 122 patients. Using this data, wedeveloped Deep Artificial Neural Network models that can predict with 92.6%accuracy, the final HbF value of a subject after undergoing HU therapy. Ourcurrent studies are focussing on forecasting a patient's HbF response, 30 daysahead of time.

Modelling of Sickle Cell Anemia Patients Response to Hydroxyurea using Artificial Neural Networks