In a clinical trial dataset that encompasses a variety of features including Mean Arterial Pressure (MAP), it's observed that the features exhibit low correlation with one another. The dataset is almost complete, with less than 1% of the MAP values missing. Aside from a few outliers, the MAP data is relatively uniformly distributed, and all other features are fully accounted for. Given these characteristics, which approach should be adopted to manage the missing MAP data most effectively?