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As part of a healthcare analytics project, you are tasked with developing a neural network model to predict medical expenses based on several patient attributes, including blood pressure. The blood pressure measurements are recorded with two decimal places of precision but need to be transformed into a more compact form of discrete categories for model input. Additionally, the distribution of blood pressure values across the patient population is highly skewed, with a significant emphasis required on ensuring that both extremely low and extremely high blood pressure measurements influence the model's predictions more than average values. Which preprocessing technique would effectively manage the precision and distribution concerns of the blood pressure data, enhancing the model's ability to accurately estimate medical expenses?